{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1483fac4-f729-45b7-98b9-a28339b47a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd   #THIS VERSION UPDATED 6-29-24 TO adopt McCartan methods throughout\n",
    "import geopandas as gpd  #see \"oldHDpartisanAnalysis\" for more sophisticated EI model, enacted calcn from shapefile\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy.stats import t, norm\n",
    "from scipy.stats import t as statsT  #confusion as I use t as integer variable\n",
    "import ast\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.affinity import translate, scale\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)\n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "def plotPolyColorWidth(inputPoly,COLOR, WIDTH):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,color=COLOR, lw=WIDTH)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,color=COLOR, lw=WIDTH)\n",
    "\n",
    "def plotPolyColorWidthStyle(inputPoly,COLOR, WIDTH, STYLE):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,color=COLOR, lw=WIDTH, ls=STYLE)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,color=COLOR, lw=WIDTH, ls=STYLE)\n",
    "\n",
    "def fillPlotColor(GEOM, COLOR):  #use COLOR = [1.-x]*3 if x is blackness\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if GEOM.geom_type == dummyPoly.geom_type:\n",
    "        x,y = GEOM.exterior.xy\n",
    "        plt.fill(x,y,facecolor=COLOR)  #COLOR could be a triplet or a standard color\n",
    "    else:\n",
    "        for geom in GEOM.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.fill(x,y,facecolor=COLOR)  \n",
    "    return\n",
    "def r3(x):\n",
    "    return round(x,3)\n",
    "def r5(x):\n",
    "    return round(x,5)\n",
    "def getLogit(x,A):\n",
    "    LOGIT = math.log(x/(1-x))\n",
    "    result = 1. - 1./(1. + math.exp(A*LOGIT))\n",
    "    return result\n",
    "\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "def votes2Seats(PARAMS, VoTeS):\n",
    "    EXPONENT, BIAS = PARAMS[0], PARAMS[1]\n",
    "    NROWS = len(VoTeS)\n",
    "    SEATSRATIO = [BIAS + (VoTeS[r]/(1.-VoTeS[r]))**EXPONENT  for r in range(NROWS) ]\n",
    "    calcSEATS = [SEATSRATIO[r] / (SEATSRATIO[r]+1.)          for r in range(NROWS) ]\n",
    "    return calcSEATS\n",
    "\n",
    "def votes2Seats2(PARAMS, VoTeS):\n",
    "    EXPONENT, BIAS = PARAMS[0], PARAMS[1]\n",
    "    NROWS = len(VoTeS)\n",
    "    SEATSRATIO = [( (VoTeS[r]+BIAS)/(1.-VoTeS[r]-BIAS) )**EXPONENT  for r in range(NROWS) ]\n",
    "    calcSEATS = [SEATSRATIO[r] / (SEATSRATIO[r]+1.)          for r in range(NROWS) ]\n",
    "    return calcSEATS\n",
    "\n",
    "def votes2SeatsError(PARAMS, VoTeS, SeAtS ):\n",
    "    \"\"\"\n",
    "    This function calculates the mean-squared-error for fitting a power-law votes-to-seats curve with geography bias\n",
    "    S / (1-S) = bias + (V/(1-V))^exponent, mostly consistent with HD paper#1's fit to 22 populous state's Congressional S(V)\n",
    "    VOTES and SEATS are passed arrays of statewide vote and seat fractions.\n",
    "    EXP is the guessed power-law exponent; seatsBIAS is the guessed seats bias, passed in as 2-D array\n",
    "    This function is the kernel of an external minimize function to optimize EXP and SEATSBIAS values\n",
    "    \"\"\"\n",
    "    EXP, seatsBIAS = PARAMS[0], PARAMS[1]\n",
    "    NROWS = len(VoTeS)\n",
    "    predictedSeats = votes2Seats(PARAMS, VoTeS)\n",
    "    rowSquareError = [(predictedSeats[r] - SeAtS[r])**2  for r in range(NROWS)]\n",
    "    totalError = np.sum(rowSquareError)\n",
    "    return totalError\n",
    "\n",
    "def votes2SeatsError2(PARAMS, VoTeS, SeAtS ):\n",
    "    \"\"\"\n",
    "    This function calculates the mean-squared-error for fitting a power-law votes-to-seats curve with geography bias\n",
    "    S / (1-S) = bias + (V/(1-V))^exponent, mostly consistent with HD paper#1's fit to 22 populous state's Congressional S(V)\n",
    "    VOTES and SEATS are passed arrays of statewide vote and seat fractions.\n",
    "    EXP is the guessed power-law exponent; seatsBIAS is the guessed seats bias, passed in as 2-D array\n",
    "    This function is the kernel of an external minimize function to optimize EXP and SEATSBIAS values\n",
    "    \"\"\"\n",
    "    EXP, seatsBIAS = PARAMS[0], PARAMS[1]\n",
    "    NROWS = len(VoTeS)\n",
    "    predictedSeats = votes2Seats2(PARAMS, VoTeS)\n",
    "    rowSquareError = [(predictedSeats[r] - SeAtS[r])**2  for r in range(NROWS)]\n",
    "    totalError = np.sum(rowSquareError)\n",
    "    return totalError\n",
    "\n",
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ebdfcbdd-30ff-48f7-bdda-23c3eb114e16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the two-letter state code; e.g. MN WI\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code computes partisan and VRA metrics of an ensemble of districts, where each district is a collection of units\n",
      "No spatial analysis here, just district metrics for HD drawing and McCartan 50-state sims vs. enacted\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the number of Congressional districts; e.g. 8 for MN 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's read in the 2020 and 2016 voting data for WI mapped to 2020 vtds\n",
      "Note, on 2-10-24, I combined 2016 and 2020 data to a single file for easier use\n",
      "In year/election 2020 Dem and GOP total votes were 1630058 1609642 for a stateGOP of 0.49685\n",
      "There are a total of 7059  vote-mapped voting districts from election year 2020 in state WI\n",
      "In year/election 2016 Dem and GOP total votes were 1382123 1404922 for a stateGOP of 0.50409\n",
      "There are a total of 7059  vote-mapped voting districts from election year 2016 in state WI\n",
      "Now ready to compute each HD's partisan stats.  Votes are based on order in vest20 file.\n",
      "First, we collapse all precinct election results into avg Dem and Rep vote totals per precinct, following Kenny\n",
      "We follow Kenny 2023 in assigning partisan votes based on arithmetic avg 2-party vote share * logmean total turnout\n",
      "All done averaging over elections. The composite statewide vote margin = 4595 with stateGOP ~ 0.50077 vs arithm average 0.50047\n",
      " I will also read in the racial VAP data from ./state_map_files/wi_2020_vtd.csv\n"
     ]
    }
   ],
   "source": [
    "STATE = input(\"enter the two-letter state code; e.g. MN\")\n",
    "print(\"this code computes partisan and VRA metrics of an ensemble of districts, where each district is a collection of units\")\n",
    "print(\"No spatial analysis here, just district metrics for HD drawing and McCartan 50-state sims vs. enacted\")\n",
    "nStateDistricts = int(input(\"enter the number of Congressional districts; e.g. 8 for MN\"))\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE,\"mapped to 2020 vtds\")\n",
    "print(\"Note, on 2-10-24, I combined 2016 and 2020 data to a single file for easier use\")\n",
    "DemCandidates, RepCandidates= [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ]\n",
    "years = [str(2020),str(2016)]\n",
    "nYears = len(years)\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "if STATE == \"CA\":\n",
    "    vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_t\"\n",
    "else:\n",
    "    vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_vtd\"\n",
    "vestDF = pd.read_csv(vestName+\".csv\")  \n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = vestDF[DemCandidates[y]].copy(), vestDF[RepCandidates[y]].copy(), vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election year\",years[y],\"in state\",STATE)\n",
    "\n",
    "print(\"Now ready to compute each HD's partisan stats.  Votes are based on order in vest20 file.\")\n",
    "print(\"First, we collapse all precinct election results into avg Dem and Rep vote totals per precinct, following Kenny\")\n",
    "print(\"We follow Kenny 2023 in assigning partisan votes based on arithmetic avg 2-party vote share * logmean total turnout\")\n",
    "unitVotes = [ ((DemVotes[0][v] + RepVotes[0][v]) * (DemVotes[1][v] + RepVotes[1][v]) ) ** 0.5 for v in range(nVTDs) ]\n",
    "unitDemFrac = [ 0.5 * ( DemVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                        DemVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "unitRepFrac = [ 0.5 * ( RepVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                        RepVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "unitLean = [1. - unitDemFrac[v] for v in range(nVTDs) ]   #above -- prevent divide by zero\n",
    "for v in range(nVTDs):\n",
    "    if (unitVotes[v] < 2):\n",
    "        unitLean[v] = 0.5  #set all low-vote precincts to tossups\n",
    "        \n",
    "unitDems = [ unitDemFrac[v] * unitVotes[v] for v in range(nVTDs) ]  #Now consolidating elections to single partisan measure by precinct\n",
    "unitReps = [ unitRepFrac[v] * unitVotes[v] for v in range(nVTDs) ]\n",
    "stateDems, stateReps = np.sum(unitDems), np.sum(unitReps)\n",
    "stateMargin = stateReps - stateDems\n",
    "stateLean = stateReps / (stateReps + stateDems)\n",
    "avgStateVotes = ((np.sum(DemVotes[0])+np.sum(RepVotes[0])) * (np.sum(DemVotes[1])+np.sum(RepVotes[1]) ) )**0.5\n",
    "print(\"All done averaging over elections. The composite statewide vote margin =\",int(stateMargin),\n",
    "      \"with stateGOP ~\", r5(stateLean),\"vs arithm average\", r5(np.average(yearLean)) )\n",
    "#print(\"Here is a scatterplot of precinct Rep votes by Dem votes\")\n",
    "#plt.scatter(unitDems,unitReps)\n",
    "#plt.show()\n",
    "\n",
    "print(\" I will also read in the racial VAP data from\",vestName+\".csv\")\n",
    "VAP = vestDF[\"vap\"]  #see oldHDpartisanAnalysis for a more sophisticated ecological inference model\n",
    "whiteVAP = vestDF[\"vap_white\"]   #here, we simply analyze in terms of VAP\n",
    "hispVAP = vestDF[\"vap_hisp\"]\n",
    "blackVAP = vestDF[\"vap_black\"]\n",
    "aianVAP = vestDF[\"vap_aian\"]\n",
    "asianVAP= vestDF[\"vap_asian\"]\n",
    "twoVAP = vestDF[\"vap_two\"]\n",
    "minVAP = [VAP[v] - whiteVAP[v] for v in range(nVTDs)]\n",
    "nEnacted = nStateDistricts\n",
    "enacted2016Dems, enacted2020Dems, enacted2016Reps, enacted2020Reps = [0.]*nEnacted,[0.]*nEnacted,[0.]*nEnacted,[0.]*nEnacted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1856cbb1-4467-45b9-8e01-589757c6e213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for WI\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MN4110opt3patchedVTDs.csv, MN4110HD2Bnopatch8nW4.csv WI7059VRApatchedVTDs.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble map describes 7059 drawn districts\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MN4110opt3patchedVTDs.csv, MN4110HD2Bnopatch8nW4.csv\")\n",
    "#infilename = \"GA_nD14tol0.01nW4.csv\"\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nDrawnDistricts = nRows\n",
    "print(\"This ensemble map describes\",nRows,\"drawn districts\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDwt = HDdf['HDweight']  #'HDwt'\n",
    "HDweight = HDwt.copy()  #nomenclature\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "if \"splitTractNo\" in HDdf.columns.values:  #this tract list came direct from polygon capture\n",
    "    HDvtdListString = HDdf['HDvtdList']\n",
    "    splitTractNo, splitTractUse = HDdf['splitTractNo'], HDdf['splitTractUse']\n",
    "    partials = [ list() for i in splitTractNo ]  #default, no partials\n",
    "    for i,t in enumerate(splitTractNo):\n",
    "        if t >= 0:  #i.e. not the -999 flag for no partials\n",
    "            partials[i] = [t]\n",
    "else :  #the HD tract lists come from contig patching\n",
    "    splitTractNo, splitTractUse = [-999 for t in range(nRows)] , [0. for t in range(nRows)]\n",
    "    HDvtdListString = HDdf['HDvtdList']\n",
    "    HDvtdList = [ast.literal_eval(L) for L in HDvtdListString]\n",
    "    if \"partials\" in HDdf.columns:\n",
    "        partialsString =  HDdf['partials']\n",
    "        partials = [ast.literal_eval(L) for L in partialsString]\n",
    "        HDvtdFracString = HDdf['HDvtdFrac']\n",
    "        HDvtdFrac = [ast.literal_eval(L) for L in HDvtdFracString]\n",
    "    else:\n",
    "        print(STATE,\"had no partial VTD column; only whole VTDs used to build HDs\")\n",
    "        partials = [list() for v in range(nRows)]\n",
    "        HDvtdFrac = [list() for v in range(nRows)]\n",
    "#paramList = HDdf['paramList'].to_list()\n",
    "#paramValues = HDdf['paramValues'].to_list()\n",
    "#STATE2 = paramValues[paramList.index(\"STATE\")]\n",
    "#if STATE2 != STATE:\n",
    "#    raise Exception(\"ERROR! We were working on\",STATE,\"but we read in the data for\",STATE2)\n",
    "#stateGOP = float(paramValues[paramValues.index(\"stateGOP\")])\n",
    "#nStateDistricts = int(paramValues[paramList.index(\"nDistricts\")])\n",
    "#print(\"This state with population\",statePop,\"was divided into\",nStateDistricts,\"districts.\")\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDwt)) )\n",
    "if abs(np.sum(HDwt) - 1) > 0.001:\n",
    "    shouldWeCorrect = input(\"enter 1 to renormalize the ensemble weight to 1, otherwise enter 0\")\n",
    "    if shouldWeCorrect == 1:\n",
    "        factor = np.sum(HDweight)\n",
    "        HDwt = [current / factor for current in HDwt]\n",
    "        HDweight = HDwt.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c28f186c-ef78-40b1-808a-5b6f0c1a74f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.001493949515116792\n",
      "1 0.11086714379583798\n",
      "2 0.4812028718248669\n",
      "3 0.31023756517434997\n",
      "4 0.014157751253410174\n",
      "5 0.002986104630677219\n",
      "6 0.06914606298652914\n",
      "7 0.009908550819211808\n"
     ]
    }
   ],
   "source": [
    "enFile = \"./enacted_map_files/CO_CD_2021.dbf\" #special CO calcn of district areas\n",
    "enDF = gpd.read_file(enFile)\n",
    "shapes = enDF[\"geometry\"]\n",
    "areas = [shape.area for shape in shapes]\n",
    "for i,shape in enumerate(shapes):\n",
    "    print(i,areas[i]/np.sum(areas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6e1f45db-0c76-417e-8c6f-815ff16d34b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we also need to pull in the '50 state sims' data to compute the span of sim results\n",
      "Kenny-Imai graphs use 66% and 95% ranges.  First N rows are the enacted districts\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wise.gm\\AppData\\Local\\Temp\\ipykernel_15300\\3428278840.py:4: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  alarmDF = pd.read_csv(alarmFile)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "McCartan lists 5000 ensemble maps of 8 districts for WI\n",
      "dNo, GOP lean, pct mVAP, blackVAP (sorted by mVAP) for enacted map according to McCartan\n",
      "7 0.60442 0.08564 0.00655\n",
      "3 0.5241 0.09051 0.01188\n",
      "6 0.58531 0.11473 0.02092\n",
      "5 0.63001 0.11648 0.01468\n",
      "8 0.58494 0.13232 0.01476\n",
      "2 0.29362 0.17996 0.03709\n",
      "1 0.51231 0.21768 0.05993\n",
      "4 0.23381 0.5296 0.29169\n",
      "Compare these mVAP stats to graph at https://github.com/alarm-redist/fifty-states/...\n"
     ]
    }
   ],
   "source": [
    "print(\"we also need to pull in the '50 state sims' data to compute the span of sim results\")\n",
    "print(\"Kenny-Imai graphs use 66% and 95% ranges.  First N rows are the enacted districts\")\n",
    "alarmFile = \"./state_ALARM_stats/\"+STATE+\"_cd_2020_stats.csv\"\n",
    "alarmDF = pd.read_csv(alarmFile)\n",
    "draw = alarmDF[\"draw\"]\n",
    "nADs = len(draw) - nEnacted #the ALARM datasset has the enacted stats in its first few rows\n",
    "enactedLean, alarmLean = [0.5]*nEnacted, [0.5]*nADs\n",
    "enacted_mVAP, alarm_mVAP, enacted_blackVAP = [0.5]*nEnacted, [0.5]*nADs, [0.5]*nEnacted\n",
    "for ii in range( len(draw)):\n",
    "    a2016Dems, a2020Dems = alarmDF[\"pre_16_dem_cli\"][ii], alarmDF[\"pre_20_dem_bid\"][ii]\n",
    "    a2016Reps, a2020Reps = alarmDF[\"pre_16_rep_tru\"][ii], alarmDF[\"pre_20_rep_tru\"][ii]\n",
    "    republicanFrac = 0.5 * ( a2016Reps / (a2016Dems + a2016Reps) + a2020Reps/(a2020Dems + a2020Reps) )\n",
    "    mVAPfrac = 1. - alarmDF[\"vap_white\"][ii]/float(alarmDF[\"total_vap\"][ii])\n",
    "    blackVAPfrac = alarmDF[\"vap_black\"][ii]/float(alarmDF[\"total_vap\"][ii])\n",
    "    if ii < nEnacted: #still in the enacted rows of the alarm datafile\n",
    "        enactedLean[ii] = republicanFrac\n",
    "        enacted_mVAP[ii] = mVAPfrac\n",
    "        enacted_blackVAP[ii] = blackVAPfrac\n",
    "    else:  #in a sims row\n",
    "        alarmLean[ii - nEnacted] = republicanFrac\n",
    "        alarm_mVAP[ii- nEnacted] = mVAPfrac\n",
    "nAlarmMaps = int(nADs / nEnacted)\n",
    "print(\"McCartan lists\",nAlarmMaps,\"ensemble maps of\",nEnacted,\"districts for\",STATE)\n",
    "print(\"dNo, GOP lean, pct mVAP, blackVAP (sorted by mVAP) for enacted map according to McCartan\")\n",
    "idx = np.argsort(enacted_mVAP)\n",
    "for ii in range(nEnacted):\n",
    "    i = idx[ii]\n",
    "    print(i+1, r5(enactedLean[i]), r5(enacted_mVAP[i]), r5(enacted_blackVAP[i]) )\n",
    "print(\"Compare these mVAP stats to graph at https://github.com/alarm-redist/fifty-states/...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c49ec872-2611-49d2-9d11-e5f5adf1a475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oops, I forgot to write the GEOID20 column to the HD output file\n",
      "Need to read in the original Census VTD file to translate HD vtd no's to vest no's.  Stand by ...\n",
      "WI 's total population is 5893718\n",
      "Our VEST file had 7059 vtds. Merging with our HD list file left us with 7059 vtds\n",
      "8 7059 40000   nDistricts (enacted, total HD, total ALARM)\n"
     ]
    }
   ],
   "source": [
    "dummyNo = [v for v in range(nVTDs)]\n",
    "targetLen = 11  #in some cases, the leading 0's in geoid's were truncated in conversion to strings\n",
    "nVTDrows = len(vestDF['GEOID20'])\n",
    "vestLen = [len(str(vestDF['GEOID20'][i])) for i in range(nVTDrows)]\n",
    "addLen  = [targetLen - vestLen[i] for i in range(nVTDrows)]\n",
    "#vestLen = len(str(vestDF['GEOID20'][0]))\n",
    "#addLen = targetLen - vestLen\n",
    "#miniVestDF = pd.DataFrame( {\"GEOID20\":[\"0\"*addLen + str(geo) for geo in vestDF['GEOID20']], \"vestNo\":dummyNo} )\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":[\"0\"*addLen[i] + str(geo) for i,geo in enumerate(vestDF['GEOID20'])], \"vestNo\":dummyNo} )\n",
    "if \"GEOID20\" in HDdf.columns:  \n",
    "    print(\"I will map vtd numbers directly from the HD output file onto vest numbers ...\")\n",
    "    #HDTL specifies Census VTD's, which may be in different order from vest. Define the translation\n",
    "    print(\"translating from HD order of precincts to VEST order\")\n",
    "    mappingDF = pd.merge(HDdf,miniVestDF,on=\"GEOID20\")\n",
    "\n",
    "else:\n",
    "    print(\"oops, I forgot to write the GEOID20 column to the HD output file\")\n",
    "    print(\"Need to read in the original Census VTD file to translate HD vtd no's to vest no's.  Stand by ...\")\n",
    "    if STATE == \"CA\":\n",
    "        popFilename = STATE.lower()+\"_pl2020_t.dbf\"\n",
    "    else:\n",
    "        popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "    tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "    tractPop = tractPopFile['P0010001']\n",
    "    statePop = np.sum(tractPop)\n",
    "    print(STATE,\"'s total population is\",statePop)\n",
    "    popLen = len(tractPopFile['GEOID20'][0])\n",
    "    addLen = targetLen - popLen\n",
    "    censusGEOID20 = [\"0\"*addLen + str(geo) for geo in tractPopFile['GEOID20'] ]\n",
    "    miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "    mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "    \n",
    "vestNo = mappingDF['vestNo']\n",
    "print(\"Our VEST file had\",len(vestDF),\"vtds. Merging with our HD list file left us with\",len(mappingDF),\"vtds\")\n",
    "if len(vestDF) != len(mappingDF):\n",
    "    print(\"    ****WARNING - these two don't match!! Our merge missed some vtd's !!!!!!!\")\n",
    "print(nStateDistricts,nDrawnDistricts, nADs, \"  nDistricts (enacted, total HD, total ALARM)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c702738b-5b7a-41b9-8817-e645cca9b6ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now summing Dem and Rep votes, minorities from precincts assigned to each HD row ...\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "Stat check: ensemble vs state Dems = 1495532 1497047\n",
      "Stat check: ensemble vs state Reps = 1492833 1501642\n",
      "Stat check: ensemble vs state Lean = 0.49955 0.50077\n",
      "Stat check: ensemble vs state vote margin -2698 4595\n",
      "here is the scatterplot of ensemble vtd usage by vtd lean, and pct minority VAP by HD lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vtd usage in HD ensemble vs. vtd pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now summing Dem and Rep votes, minorities from precincts assigned to each HD row ...\")\n",
    "districtDemVotes, districtRepVotes, district_mVAP = [0.]*nDrawnDistricts , [0.]*nDrawnDistricts, [0.]*nDrawnDistricts\n",
    "districtLean, districtVoteMargin =  [0.5]*nDrawnDistricts , [0.]*nDrawnDistricts\n",
    "vtdUse = [0.]*nVTDs\n",
    "ensembleDems, ensembleReps =        0., 0.\n",
    "for d in range(nDrawnDistricts):\n",
    "    if d%1000 == 1:\n",
    "        print(\"working on HD ensemble district no\",d)\n",
    "    dVAP, d_whiteVAP = 0., 0.\n",
    "    if len(HDvtdList[d]) > 0:\n",
    "        for tt in HDvtdList[d] + partials[d]:\n",
    "            USE = 1.  #default, vtd fully used in this list\n",
    "            if tt in partials[d]:  \n",
    "                USE = 0.   #don't double-count\n",
    "                if tt not in HDvtdList[d]:  #this only appears in the partial list\n",
    "                    jj = partials[d].index(tt)\n",
    "                    USE = HDvtdFrac[d][jj]\n",
    "            vNo = vestNo[tt]  #must apply mapping since ensemble vtd no's in different order\n",
    "            districtDemVotes[d] += USE*unitDems[vNo]\n",
    "            districtRepVotes[d] += USE*unitReps[vNo]\n",
    "            dVAP                += USE*     VAP[vNo] \n",
    "            d_whiteVAP          += USE*whiteVAP[vNo]\n",
    "            ensembleDems        += USE*unitDems[vNo] * nStateDistricts*HDwt[d]\n",
    "            ensembleReps        += USE*unitReps[vNo] * nStateDistricts*HDwt[d]  \n",
    "            \n",
    "            vtdUse[vNo] += USE*nStateDistricts*HDwt[d]\n",
    "            \n",
    "        districtLean[d] = districtRepVotes[d] / (districtDemVotes[d] + districtRepVotes[d] )\n",
    "        districtVoteMargin[d] = districtRepVotes[d] - districtDemVotes[d]\n",
    "        district_mVAP[d]      = 1. - d_whiteVAP/float(dVAP)\n",
    "\n",
    "            \n",
    "ensembleLean = ensembleReps / (ensembleDems + ensembleReps)\n",
    "ensembleMargin = ensembleReps - ensembleDems\n",
    "print(\"Stat check: ensemble vs state Dems =\",int(ensembleDems),      int(stateDems))\n",
    "print(\"Stat check: ensemble vs state Reps =\",int(ensembleReps),      int(stateReps))\n",
    "print(\"Stat check: ensemble vs state Lean =\",r5(ensembleLean),        r5(stateLean))\n",
    "print(\"Stat check: ensemble vs state vote margin\",int(ensembleMargin),int(stateMargin))\n",
    "print(\"here is the scatterplot of ensemble vtd usage by vtd lean, and pct minority VAP by HD lean\")\n",
    "SIZE = [2+3.*math.log(unitDems[v]+unitReps[v]+1.) for v in range(nVTDs)] #scale dots by vote number\n",
    "plt.scatter(unitLean, vtdUse, s=SIZE,label=\"vtdUse\")\n",
    "plt.scatter(districtLean, district_mVAP, marker=\"x\",s=3,label=\"mVAP\")\n",
    "plt.xlabel(\"vtd or district lean\")\n",
    "plt.ylabel(\"vtd usage or district pct minority\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.scatter(tractPop,vtdUse)\n",
    "print(\"vtd usage in HD ensemble vs. vtd pop\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "22210a5a-81d2-4fc5-a025-53f3f42e9c4a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we calculate number of opportunity districts for enacted and ensembles\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter WI's minority VAP threshold (e.g. 0.4) 0.4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of enacted, HD-expected, ALARM-expected opportunity districts in WI are 1.0 1.01993 0.9286\n",
      " ... using a simple mVAP threshold of 0.4 to define an opportunity district, regardless of lean\n"
     ]
    }
   ],
   "source": [
    "print(\"Now we calculate number of opportunity districts for enacted and ensembles\")\n",
    "mVAPthreshold = float(input(\"enter \"+STATE+\"'s minority VAP threshold (e.g. 0.4)\"))\n",
    "nEnsembleODs, nEnactedODs, nAlarmODs = 0., 0., 0.\n",
    "for d in range(nDrawnDistricts):\n",
    "    if district_mVAP[d] >= mVAPthreshold:\n",
    "        nEnsembleODs += HDweight[d] * nStateDistricts\n",
    "for d in range(nEnacted):\n",
    "    if enacted_mVAP[d] >= mVAPthreshold:\n",
    "        nEnactedODs += 1\n",
    "for d in range(nADs):\n",
    "    if alarm_mVAP[d] >= mVAPthreshold:\n",
    "        nAlarmODs += 1./float(nAlarmMaps)\n",
    "print(\"The total number of enacted, HD-expected, ALARM-expected opportunity districts in\",STATE,\"are\",\n",
    "      r5(nEnactedODs),r5(nEnsembleODs), r5(nAlarmODs) )\n",
    "print(\" ... using a simple mVAP threshold of\",mVAPthreshold,\"to define an opportunity district, regardless of lean\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b9f4579f-643d-4662-ae1b-568a35d98f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram of districts by minority VAP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Histogram of districts by minority VAP\")\n",
    "plt.hist(district_mVAP,weights=HDweight,histtype=\"step\",label=\"HD mVAP\",bins=20)\n",
    "plt.hist(alarm_mVAP,weights = [1./nADs]*nADs,histtype=\"step\",label=\"ALARM\",bins=20)\n",
    "plt.hist(enacted_mVAP, weights = [1./float(nEnacted)]*nEnacted,label=\"enacted\",bins=20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8dec4046-eaf6-45b0-b8de-24e44b24419d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating partisan seat expectations ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using norm model, GOP nDistricts, the GOP seat fraction, responsiveness are 5.13752 0.64219 2.33522\n",
      "Using logit model, these values are                                      4.77684 0.59711 2.29952\n",
      "....comparatively for the enacted map....\n",
      "    norm model, GOP nDistricts, the GOP seat fraction, responsiveness are 5.30906 0.66363 2.53009\n",
      "   enacted-logit, these values are                                      4.94932 0.61867 2.60302\n",
      "the average GOP seat fraction across the ALARM simulations is 0.61702\n",
      "The quantiles of the ALARM simulation set's GOP seat fraction are\n",
      "0.025 0.55428\n",
      "0.17 0.59965\n",
      "0.5 0.62239\n",
      "0.83 0.6363\n",
      "0.975 0.64569\n",
      "Partisan gerrymandering total, fractional seats relative to ALARM median = -0.02983 -0.00373\n",
      "Using HD, we compute a total of 0.17248 extra seats to GOP from GMing, or a fractional shift of 0.02156 using logit model with nu, nuSigma 3.8 0.221\n",
      "****\n",
      "   Alt'ly, HD method computes a total of 0.17154 extra seats to GOP from GMing, or a fractional shift of 0.02144 using norm model with normU 0.04\n"
     ]
    }
   ],
   "source": [
    "params = ['nDistricts', nStateDistricts,\" \",\"nHDs\",nDrawnDistricts,\" \"]\n",
    "normU = 0.04  #e.g. Nagle-Ramsay normal seat uncertainty; true vote = mean vote + N(U)\n",
    "logitA = 10.4    #e.g. Kenny logit seat uncertainty: logit S = A logit V\n",
    "print(\"Calculating partisan seat expectations ...\")\n",
    "nu, nuSigma = 3.8, 0.221  #see Imai-logit.ipynb for how we fit the Kenny-Imai model to a single t distro\n",
    "\n",
    "normSeatExp    = [norm.cdf((districtLean[d] - 0.5)/normU)                            for d in range(nDrawnDistricts) ]\n",
    "normSeatEn     = [norm.cdf((enactedLean[d] - 0.5)/normU)                            for d in range(nEnacted) ]\n",
    "#logitSeatExp = [getLogit(districtLean[d],logitA)                                   for d in range(nDrawnDistricts) ]\n",
    "KennySeatExp = [statsT.cdf(math.log(districtLean[d]/(1.-districtLean[d])), nu,0,nuSigma) for d in range(nDrawnDistricts) ]\n",
    "KennySeatAlarm = [statsT.cdf(math.log(alarmLean[d]/(1.-alarmLean[d])), nu,0,nuSigma) for d in range(nADs) ]\n",
    "KennySeatEn = [statsT.cdf(math.log(enactedLean[d] / (1.-enactedLean[d])), nu,0,nuSigma) for d in range(nEnacted) ]\n",
    "nExpectedODs, nEnactedODs, nAlarmODs = 0., 0., 0.  #these are the number of opportunity districts.\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"HD seat expectation\", ylabel=\"cumulative distro\")\n",
    "plt.hist(normSeatExp, bins=50,weights=HDwt,histtype=\"step\",cumulative=\"True\",label=\"U=\"+str(normU))\n",
    "plt.hist(KennySeatExp,bins=50,weights=HDwt,histtype=\"step\",cumulative=\"True\",label=\"logit\"+str(logitA))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"HD seat expectation\", ylabel=\"cumulative distro\")\n",
    "plt.hist(normSeatEn, bins=50,              histtype=\"step\",cumulative=\"True\",label=\"Uenacted\")\n",
    "plt.hist(KennySeatEn,bins=50,              histtype=\"step\",cumulative=\"True\",label=\"logitEnac\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "totNormSeatExp =  np.sum( [normSeatExp[d]  * HDweight[d] for d in range(nDrawnDistricts) ] )\n",
    "totNormSeatEn = np.sum(normSeatEn) / float(nEnacted)  #simple weighting\n",
    "totLogitSeatExp = np.sum( [KennySeatExp[d] * HDweight[d] for d in range(nDrawnDistricts) ] )\n",
    "totLogitSeatEn = np.sum(KennySeatEn) / float(nEnacted)\n",
    "avgLogitSeatAlarm = np.sum(KennySeatAlarm) / float(nADs)\n",
    "logitSeatPerAlarmMap = [np.sum( KennySeatAlarm[nEnacted*i:nEnacted*(i+1)] )/float(nEnacted) for i in range(nAlarmMaps) ]\n",
    "params = params + [\"totLogitSeatExp\", totLogitSeatExp,\" \", \"totLogitSeatEn\",totLogitSeatEn,\" \"]\n",
    "\n",
    "SWING = 0.01  #arbitrary small swing for estimating responsiveness with fractional seat model applied.\n",
    "lowTNSexp =  np.sum( [HDweight[d] * norm.cdf((districtLean[d] - SWING - 0.5)/normU) for d in range(nDrawnDistricts) ] )\n",
    "highTNSexp = np.sum( [HDweight[d] * norm.cdf((districtLean[d] + SWING - 0.5)/normU) for d in range(nDrawnDistricts) ] )\n",
    "lowTNSen =  np.sum( [1./nEnacted * norm.cdf((enactedLean[d] - SWING - 0.5)/normU) for d in range(nEnacted) ] )\n",
    "highTNSen = np.sum( [1./nEnacted * norm.cdf((enactedLean[d] + SWING - 0.5)/normU) for d in range(nEnacted) ] )\n",
    "\n",
    "lowTLSexp =  np.sum( [HDweight[d] * getLogit(districtLean[d] - SWING,logitA) for d in range(nDrawnDistricts) ] )\n",
    "highTLSexp = np.sum( [HDweight[d] * getLogit(districtLean[d] + SWING,logitA) for d in range(nDrawnDistricts) ] )\n",
    "lowTLSen =  np.sum( [1./nEnacted * getLogit(enactedLean[d] - SWING,logitA) for d in range(nEnacted) ] )\n",
    "highTLSen = np.sum( [1./nEnacted * getLogit(enactedLean[d] + SWING,logitA) for d in range(nEnacted) ] )\n",
    "    \n",
    "normRespExp =  (highTNSexp - lowTNSexp) / (2.*SWING)\n",
    "logitRespExp = (highTLSexp - lowTLSexp) / (2.*SWING)\n",
    "normRespEn =  (highTNSen - lowTNSen) / (2.*SWING)\n",
    "logitRespEn = (highTLSen - lowTLSen) / (2.*SWING)\n",
    "\n",
    "params = params + [\"logitRespExp\",  logitRespExp, \" \", \"logitRespEn\",logitRespEn,\" \"]\n",
    "\n",
    "normGOPnDistricts, logitGOPnDistricts     = nStateDistricts * totNormSeatExp, nStateDistricts *totLogitSeatExp\n",
    "normEnGOPnDistricts, logitEnGOPnDistricts = nStateDistricts * totNormSeatEn , nStateDistricts *totLogitSeatEn\n",
    "\n",
    "print(\"Using norm model, GOP nDistricts, the GOP seat fraction, responsiveness are\",r5(normGOPnDistricts),r5(totNormSeatExp),r5(normRespExp) )\n",
    "print(\"Using logit model, these values are                                     \",r5(logitGOPnDistricts),r5(totLogitSeatExp),r5(logitRespExp) )\n",
    "print(\"....comparatively for the enacted map....\")\n",
    "print(\"    norm model, GOP nDistricts, the GOP seat fraction, responsiveness are\",r5(normEnGOPnDistricts),r5(totNormSeatEn),r5(normRespEn) )\n",
    "\n",
    "print(\"   enacted-logit, these values are                                     \",\n",
    "      r5(logitEnGOPnDistricts), r5(totLogitSeatEn ),r5(logitRespEn) )\n",
    "print(\"the average GOP seat fraction across the ALARM simulations is\",r5(avgLogitSeatAlarm))\n",
    "qtiles = [0.025, 0.17, 0.50, 0.83, 0.975]\n",
    "print(\"The quantiles of the ALARM simulation set's GOP seat fraction are\")\n",
    "for q in qtiles:\n",
    "    print(q,r5(np.quantile(logitSeatPerAlarmMap,q ) ) )\n",
    "normSeatGM = normEnGOPnDistricts - normGOPnDistricts\n",
    "logitSeatGM = logitEnGOPnDistricts - logitGOPnDistricts\n",
    "alarmSeatGM = logitEnGOPnDistricts - nEnacted*np.quantile(logitSeatPerAlarmMap,0.5)\n",
    "print(\"Partisan gerrymandering total, fractional seats relative to ALARM median =\", \n",
    "      r5(alarmSeatGM),r5(alarmSeatGM/nStateDistricts) )\n",
    "print(\"Using HD, we compute a total of\",r5(logitSeatGM),\"extra seats to GOP from GMing, or a fractional shift of\",\n",
    "      r5(logitSeatGM/nStateDistricts),\"using logit model with nu, nuSigma\",nu, nuSigma )\n",
    "print(\"****\")\n",
    "print(\"   Alt'ly, HD method computes a total of\",r5(normSeatGM),\"extra seats to GOP from GMing, or a fractional shift of\",\n",
    "      r5(normSeatGM/nStateDistricts),\"using norm model with normU\",normU )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "faf1c073-e7ea-4abf-b7f0-6f5529e05ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "repeating above for a set of normal uncertainties\n",
      "Using norm 0.04 , GOP nDistricts, the GOP seat fraction, responsiveness are 3.31235 0.33124 2.85448\n",
      "Using norm 0.02 , GOP nDistricts, the GOP seat fraction, responsiveness are 3.40126 0.34013 2.7309\n",
      "Using norm 0.01 , GOP nDistricts, the GOP seat fraction, responsiveness are 3.46155 0.34615 2.74662\n"
     ]
    }
   ],
   "source": [
    "print(\"repeating above for a set of normal uncertainties\")\n",
    "\n",
    "for normU in [0.04, 0.02, 0.01]:\n",
    "\n",
    "    normSeatExp    = [norm.cdf((districtLean[d] - 0.5)/normU)                            for d in range(nDrawnDistricts) ]\n",
    "    normSeatEn     = [norm.cdf((enactedLean[d] - 0.5)/normU)                            for d in range(nEnacted) ]\n",
    "\n",
    "    totNormSeatExp =  np.sum( [normSeatExp[d]  * HDweight[d] for d in range(nDrawnDistricts) ] )\n",
    "    totNormSeatEn = np.sum(normSeatEn) / float(nEnacted)  #simple weighting\n",
    "\n",
    "\n",
    "    SWING = 0.01  #arbitrary small swing for estimating responsiveness with fractional seat model applied.\n",
    "    lowTNSexp =  np.sum( [HDweight[d] * norm.cdf((districtLean[d] - SWING - 0.5)/normU) for d in range(nDrawnDistricts) ] )\n",
    "    highTNSexp = np.sum( [HDweight[d] * norm.cdf((districtLean[d] + SWING - 0.5)/normU) for d in range(nDrawnDistricts) ] )\n",
    "    lowTNSen =  np.sum( [1./nEnacted * norm.cdf((enactedLean[d] - SWING - 0.5)/normU) for d in range(nEnacted) ] )\n",
    "    highTNSen = np.sum( [1./nEnacted * norm.cdf((enactedLean[d] + SWING - 0.5)/normU) for d in range(nEnacted) ] )\n",
    "    \n",
    "    normRespExp =  (highTNSexp - lowTNSexp) / (2.*SWING)\n",
    "    normRespEn =  (highTNSen - lowTNSen) / (2.*SWING)\n",
    "    params = params + [\"normRespExp\"+str(normU),normRespExp,\" \",\"normRespEn\"+str(normU),normRespEn,\" \"]\n",
    "\n",
    "    normGOPnDistricts, logitGOPnDistricts     = nStateDistricts * totNormSeatExp, nStateDistricts *totLogitSeatExp\n",
    "    normEnGOPnDistricts, logitEnGOPnDistricts = nStateDistricts * totNormSeatEn , nStateDistricts *totLogitSeatEn\n",
    "\n",
    "    print(\"Using norm\",normU,\", GOP nDistricts, the GOP seat fraction, responsiveness are\",r5(normGOPnDistricts),r5(totNormSeatExp),r5(normRespExp) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e58fc10c-2120-4626-a2bd-0d1fc4109532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we write the HD district results to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Now we write the HD district results to a file\")\n",
    "vtdGEOID = mappingDF['GEOID20'].to_list()\n",
    "for i in range(nDrawnDistricts - len(vtdGEOID) ): #need to add rows for cut districts\n",
    "    vtdGEOID.append(\"cutDistrict\"+str(i))\n",
    "for i in range(len(vtdGEOID) - len(params)):\n",
    "    params.append(\"-\")\n",
    "new_data = {'GOPlean': districtLean, 'GEOID20': vtdGEOID, 'params':params }\n",
    "outDF = HDdf.assign(**new_data)\n",
    "outPath = \"./2024state_HD_output/\"+STATE+\"_HDstats.csv\"\n",
    "outDF.to_csv(outPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0e3da9-b4b1-4c9d-bd56-aaa7a2b94510",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4a1770-59ee-417b-a98a-3b5d2b674a67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding in a few more district stats to the final HDstats table by state\n",
      "In year/election 2020 AZ Dem and GOP total votes were 1672113 1661655 for a stateGOP of 0.49843\n",
      "In year/election 2016 AZ Dem and GOP total votes were 1161138 1252385 for a stateGOP of 0.5189\n",
      "Averagning over elections, the composite AZ statewide vote margin = 48689 with stateGOP ~ 0.5086 vs arithm average 0.50867\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1388434 1437561 0.50869\n",
      "In year/election 2020 CA Dem and GOP total votes were 11104861 6002732 for a stateGOP of 0.35088\n",
      "In year/election 2016 CA Dem and GOP total votes were 8748378 4481928 for a stateGOP of 0.33876\n",
      "Averagning over elections, the composite CA statewide vote margin = -4632773 with stateGOP ~ 0.34487 vs arithm average 0.34482\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD ensemble district no 9001\n",
      "total ensemble Dems and Reps, ensemble state lean are 9794759 5160478 0.34506\n",
      "In year/election 2020 CO Dem and GOP total votes were 1804347 1364598 for a stateGOP of 0.43062\n",
      "In year/election 2016 CO Dem and GOP total votes were 1338867 1202477 for a stateGOP of 0.47317\n",
      "Averagning over elections, the composite CO statewide vote margin = -270805 with stateGOP ~ 0.45202 vs arithm average 0.45189\n",
      "translating from HD order of precincts to VEST order\n",
      "CO had no partial VTD column; only whole VTDs used to build HDs\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1556445 1264906 0.44833\n",
      "In year/election 2020 FL Dem and GOP total votes were 5296164 5667685 for a stateGOP of 0.51694\n",
      "In year/election 2016 FL Dem and GOP total votes were 4500465 4615841 for a stateGOP of 0.50633\n",
      "Averagning over elections, the composite FL statewide vote margin = 233263 with stateGOP ~ 0.51171 vs arithm average 0.51164\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "total ensemble Dems and Reps, ensemble state lean are 4838407 5058081 0.5111\n",
      "In year/election 2020 GA Dem and GOP total votes were 2474466 2461834 for a stateGOP of 0.49872\n",
      "In year/election 2016 GA Dem and GOP total votes were 1877962 2089101 for a stateGOP of 0.52661\n",
      "Averagning over elections, the composite GA statewide vote margin = 115398 with stateGOP ~ 0.51307 vs arithm average 0.51267\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2141456 2249450 0.5123\n",
      "In year/election 2020 IL Dem and GOP total votes were 3471912 2446894 for a stateGOP of 0.41341\n",
      "In year/election 2016 IL Dem and GOP total votes were 3090729 2146016 for a stateGOP of 0.4098\n",
      "Averagning over elections, the composite IL statewide vote margin = -976472 with stateGOP ~ 0.41209 vs arithm average 0.4116\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD ensemble district no 9001\n",
      "working on HD ensemble district no 10001\n",
      "total ensemble Dems and Reps, ensemble state lean are 3256100 2298097 0.41376\n",
      "In year/election 2020 IN Dem and GOP total votes were 1242499 1729857 for a stateGOP of 0.58198\n",
      "In year/election 2016 IN Dem and GOP total votes were 1033154 1557242 for a stateGOP of 0.60116\n",
      "Averagning over elections, the composite IN statewide vote margin = 509813 with stateGOP ~ 0.59207 vs arithm average 0.59157\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1117589 1639284 0.59462\n",
      "In year/election 2020 MA Dem and GOP total votes were 2382183 1167184 for a stateGOP of 0.32884\n",
      "In year/election 2016 MA Dem and GOP total votes were 1995182 1090877 for a stateGOP of 0.35349\n",
      "Averagning over elections, the composite MA statewide vote margin = -1048153 with stateGOP ~ 0.34155 vs arithm average 0.34116\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2183413 1121905 0.33942\n",
      "In year/election 2020 MD Dem and GOP total votes were 1984904 976351 for a stateGOP of 0.32971\n",
      "In year/election 2016 MD Dem and GOP total votes were 1677884 943134 for a stateGOP of 0.35983\n",
      "Averagning over elections, the composite MD statewide vote margin = -854216 with stateGOP ~ 0.34583 vs arithm average 0.34477\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1809674 948849 0.34397\n",
      "In year/election 2020 MI Dem and GOP total votes were 2804034 2649859 for a stateGOP of 0.48587\n",
      "In year/election 2016 MI Dem and GOP total votes were 2268840 2279544 for a stateGOP of 0.50118\n",
      "Averagning over elections, the composite MI statewide vote margin = -60459 with stateGOP ~ 0.49392 vs arithm average 0.49352\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2493480 2424822 0.49302\n",
      "In year/election 2020 MN Dem and GOP total votes were 1717072 1484060 for a stateGOP of 0.4636\n",
      "In year/election 2016 MN Dem and GOP total votes were 1367713 1322947 for a stateGOP of 0.49168\n",
      "Averagning over elections, the composite MN statewide vote margin = -130381 with stateGOP ~ 0.47776 vs arithm average 0.47764\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1529944 1386585 0.47542\n",
      "In year/election 2020 MO Dem and GOP total votes were 1253000 1718714 for a stateGOP of 0.57836\n",
      "In year/election 2016 MO Dem and GOP total votes were 1071064 1594495 for a stateGOP of 0.59818\n",
      "Averagning over elections, the composite MO statewide vote margin = 501428 with stateGOP ~ 0.58988 vs arithm average 0.58827\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1141574 1631313 0.58831\n",
      "In year/election 2020 NC Dem and GOP total votes were 2684276 2758734 for a stateGOP of 0.50684\n",
      "In year/election 2016 NC Dem and GOP total votes were 2189306 2362597 for a stateGOP of 0.51903\n",
      "Averagning over elections, the composite NC statewide vote margin = 134315 with stateGOP ~ 0.51352 vs arithm average 0.51294\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2418665 2547461 0.51297\n",
      "In year/election 2020 NJ Dem and GOP total votes were 2607921 1882732 for a stateGOP of 0.41926\n",
      "In year/election 2016 NJ Dem and GOP total votes were 2148267 1601915 for a stateGOP of 0.42716\n",
      "Averagning over elections, the composite NJ statewide vote margin = -593180 with stateGOP ~ 0.42447 vs arithm average 0.42321\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2261603 1666825 0.4243\n",
      "In year/election 2020 NY Dem and GOP total votes were 5209888 3245516 for a stateGOP of 0.38384\n",
      "In year/election 2016 NY Dem and GOP total votes were 4524696 2815519 for a stateGOP of 0.38357\n",
      "Averagning over elections, the composite NY statewide vote margin = -1796251 with stateGOP ~ 0.3852 vs arithm average 0.38371\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD ensemble district no 9001\n",
      "working on HD ensemble district no 10001\n",
      "working on HD ensemble district no 11001\n",
      "working on HD ensemble district no 12001\n",
      "working on HD ensemble district no 13001\n",
      "working on HD ensemble district no 14001\n",
      "total ensemble Dems and Reps, ensemble state lean are 4815522 3010006 0.38464\n",
      "In year/election 2020 OH Dem and GOP total votes were 2678283 3154724 for a stateGOP of 0.54084\n",
      "In year/election 2016 OH Dem and GOP total votes were 2392589 2840665 for a stateGOP of 0.54281\n",
      "Averagning over elections, the composite OH statewide vote margin = 468339 with stateGOP ~ 0.54251 vs arithm average 0.54183\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2514602 2994034 0.54352\n",
      "In year/election 2020 PA Dem and GOP total votes were 3457443 3376900 for a stateGOP of 0.49411\n",
      "In year/election 2016 PA Dem and GOP total votes were 2926136 2970913 for a stateGOP of 0.5038\n",
      "Averagning over elections, the composite PA statewide vote margin = -6456 with stateGOP ~ 0.49949 vs arithm average 0.49895\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD ensemble district no 9001\n",
      "total ensemble Dems and Reps, ensemble state lean are 3165528 3172438 0.50055\n",
      "In year/election 2020 TN Dem and GOP total votes were 1143683 1852424 for a stateGOP of 0.61828\n",
      "In year/election 2016 TN Dem and GOP total votes were 870661 1522865 for a stateGOP of 0.63624\n",
      "Averagning over elections, the composite TN statewide vote margin = 681993 with stateGOP ~ 0.62762 vs arithm average 0.62726\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "total ensemble Dems and Reps, ensemble state lean are 998584 1673209 0.62625\n",
      "In year/election 2020 TX Dem and GOP total votes were 5259258 5890552 for a stateGOP of 0.52831\n",
      "In year/election 2016 TX Dem and GOP total votes were 3877843 4684837 for a stateGOP of 0.54712\n",
      "Averagning over elections, the composite TX statewide vote margin = 745764 with stateGOP ~ 0.53836 vs arithm average 0.53772\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD ensemble district no 9001\n",
      "total ensemble Dems and Reps, ensemble state lean are 4479747 5244173 0.53931\n",
      "In year/election 2020 VA Dem and GOP total votes were 2412148 1961628 for a stateGOP of 0.4485\n",
      "In year/election 2016 VA Dem and GOP total votes were 1981471 1769442 for a stateGOP of 0.47174\n",
      "Averagning over elections, the composite VA statewide vote margin = -314590 with stateGOP ~ 0.46094 vs arithm average 0.46012\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2176834 1852894 0.45981\n",
      "In year/election 2020 WA Dem and GOP total votes were 2367943 1583633 for a stateGOP of 0.40076\n",
      "In year/election 2016 WA Dem and GOP total votes were 1742334 1221620 for a stateGOP of 0.41216\n",
      "Averagning over elections, the composite WA statewide vote margin = -635793 with stateGOP ~ 0.40657 vs arithm average 0.40646\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "total ensemble Dems and Reps, ensemble state lean are 2007679 1385712 0.40836\n",
      "In year/election 2020 WI Dem and GOP total votes were 1630058 1609642 for a stateGOP of 0.49685\n",
      "In year/election 2016 WI Dem and GOP total votes were 1382123 1404922 for a stateGOP of 0.50409\n",
      "Averagning over elections, the composite WI statewide vote margin = 4595 with stateGOP ~ 0.50077 vs arithm average 0.50047\n",
      "translating from HD order of precincts to VEST order\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "total ensemble Dems and Reps, ensemble state lean are 1495532 1492833 0.49955\n"
     ]
    }
   ],
   "source": [
    "print(\"adding in a few more district stats to the final HDstats table by state\")  \n",
    "stateList = [\"az\",\"ca\",\"co\",\"fl\",\"ga\",\"il\",\"in\",\"ma\",\"md\",\"mi\",\"mn\",\"mo\",\"nc\",\"nj\",\"ny\",\"oh\",\"pa\",\"tn\",\"tx\",\"va\",\"wa\",\"wi\"]\n",
    "nD = [9,52,8,28,14,17,9,9,8,13,8,8,14,12,26,15,17,9,38,11,10,8]\n",
    "#stateList = [\"az\"] #testing\n",
    "for kk,state in enumerate(stateList):\n",
    "    STATE = state.upper()\n",
    "    nStateDistricts = nD[kk]\n",
    "    inDF = pd.read_csv(\"./2024state_HD_output/\"+STATE+\"_HDstats.csv\")    \n",
    "    DemCandidates, RepCandidates= [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ]\n",
    "    years = [str(2020),str(2016)]\n",
    "    nYears = len(years)\n",
    "    unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "    DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "    if STATE == \"CA\":\n",
    "        vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_t\"\n",
    "    else:\n",
    "        vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_vtd\"\n",
    "    vestDF = pd.read_csv(vestName+\".csv\")  \n",
    "\n",
    "    for y,year in enumerate(years):\n",
    "        DemVotes[y], RepVotes[y], unitIDs[y] = vestDF[DemCandidates[y]].copy(), vestDF[RepCandidates[y]].copy(), vestDF[(\"GEOID20\")]\n",
    "        vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "        yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "        print(\"In year/election\",year,STATE,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\n",
    "              \"for a stateGOP of\",r5(yearLean[y]) )\n",
    "        nVTDs = len(DemVotes[y])\n",
    "        #print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election year\",years[y],\"in state\",STATE)\n",
    "\n",
    "    #print(\"Votes are based on order in vest20 file.\")\n",
    "    #print(\"First, we collapse all precinct election results into avg Dem and Rep vote totals per precinct, following Kenny\")\n",
    "    #print(\"We follow Kenny 2023 in assigning partisan votes based on arithmetic avg 2-party vote share * logmean total turnout\")\n",
    "    unitVotes = [ ((DemVotes[0][v] + RepVotes[0][v]) * (DemVotes[1][v] + RepVotes[1][v]) ) ** 0.5 for v in range(nVTDs) ]\n",
    "    unitDemFrac = [ 0.5 * ( DemVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                            DemVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "    unitRepFrac = [ 0.5 * ( RepVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                            RepVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "    unitLean = [1. - unitDemFrac[v] for v in range(nVTDs) ]   #above -- prevent divide by zero\n",
    "    for v in range(nVTDs):\n",
    "        if (unitVotes[v] < 2):\n",
    "            unitLean[v] = 0.5  #set all low-vote precincts to tossups\n",
    "            \n",
    "    unitDems = [ unitDemFrac[v] * unitVotes[v] for v in range(nVTDs) ]  #Now consolidating elections to single partisan measure by precinct\n",
    "    unitReps = [ unitRepFrac[v] * unitVotes[v] for v in range(nVTDs) ]\n",
    "    stateDems, stateReps = np.sum(unitDems), np.sum(unitReps)\n",
    "    stateMargin = stateReps - stateDems\n",
    "    stateLean = stateReps / (stateReps + stateDems)\n",
    "    avgStateVotes = ((np.sum(DemVotes[0])+np.sum(RepVotes[0])) * (np.sum(DemVotes[1])+np.sum(RepVotes[1]) ) )**0.5\n",
    "    print(\"Averagning over elections, the composite\",STATE,\"statewide vote margin =\",int(stateMargin),\n",
    "          \"with stateGOP ~\", r5(stateLean),\"vs arithm average\", r5(np.average(yearLean)) )\n",
    "    \n",
    "    VAP = vestDF[\"vap\"]  #see oldHDpartisanAnalysis for a more sophisticated ecological inference model\n",
    "    whiteVAP = vestDF[\"vap_white\"]   #here, we simply analyze in terms of VAP\n",
    "    hispVAP = vestDF[\"vap_hisp\"]\n",
    "    blackVAP = vestDF[\"vap_black\"]\n",
    "    aianVAP = vestDF[\"vap_aian\"]\n",
    "    asianVAP= vestDF[\"vap_asian\"]\n",
    "    twoVAP = vestDF[\"vap_two\"]\n",
    "    #minVAP = [VAP[v] - whiteVAP[v] for v in range(nVTDs)]  #don't care here\n",
    "    nEnacted = nStateDistricts\n",
    "    enacted2016Dems, enacted2020Dems, enacted2016Reps, enacted2020Reps = [0.]*nEnacted,[0.]*nEnacted,[0.]*nEnacted,[0.]*nEnacted\n",
    "\n",
    "    dummyNo = [v for v in range(nVTDs)]\n",
    "    targetLen = 11  #in some cases, the leading 0's in geoid's were truncated in conversion to strings\n",
    "    nVTDrows = len(vestDF['GEOID20'])\n",
    "    vestLen = [len(str(vestDF['GEOID20'][i])) for i in range(nVTDrows)]\n",
    "    addLen  = [targetLen - vestLen[i] for i in range(nVTDrows)]\n",
    "    miniVestDF = pd.DataFrame( {\"GEOID20\":[\"0\"*addLen[i] + str(geo) for i,geo in enumerate(vestDF['GEOID20'])], \"vestNo\":dummyNo} )      \n",
    "    \n",
    "    inLen = [len(str(inDF['GEOID20'][i])) for i in range(nVTDrows)] \n",
    "    addLen  = [targetLen - inLen[i] for i in range(nVTDrows)]  #below - [:nVTDrows] in case we have cut districts\n",
    "    mini_inDF = pd.DataFrame( {\"GEOID20\":[ \"0\"*addLen[i] + str(geo) for i,geo in enumerate(inDF['GEOID20'][:nVTDrows])]} )        \n",
    "    print(\"translating from HD order of precincts to VEST order\")\n",
    "    mappingDF = pd.merge(mini_inDF,miniVestDF,on=\"GEOID20\")\n",
    "    vestNo = mappingDF['vestNo']\n",
    "    #print(\"Now we convert each HD's vtdlist string and compute district stats\")\n",
    "    \n",
    "    nRows = len(inDF)\n",
    "    nDrawnDistricts = nRows\n",
    "    HDwt, HDweight = inDF['HDweight'], inDF['HDweight']    \n",
    "    splitTractNo, splitTractUse = [-999 for t in range(nRows)] , [0. for t in range(nRows)]\n",
    "    HDvtdListString = inDF['HDvtdList']\n",
    "    HDvtdList = [ast.literal_eval(L) for L in HDvtdListString]\n",
    "    if \"partials\" in inDF.columns:\n",
    "        partialsString =  inDF['partials']\n",
    "        partials = [ast.literal_eval(L) for L in partialsString]\n",
    "        HDvtdFracString = inDF['HDvtdFrac']\n",
    "        HDvtdFrac = [ast.literal_eval(L) for L in HDvtdFracString]\n",
    "    else:\n",
    "        print(STATE,\"had no partial VTD column; only whole VTDs used to build HDs\")\n",
    "        partials = [list() for v in range(nRows)]\n",
    "        HDvtdFrac = [list() for v in range(nRows)]\n",
    "\n",
    "    #print(\"Now summing Dem and Rep votes, minorities from precincts assigned to each HD row ...\")\n",
    "    districtDemVotes, districtRepVotes, district_mVAP = [0.]*nDrawnDistricts , [0.]*nDrawnDistricts, [0.]*nDrawnDistricts\n",
    "    districtLean, districtVoteMargin =  [0.5]*nDrawnDistricts , [0.]*nDrawnDistricts\n",
    "    vtdUse = [0.]*nVTDs\n",
    "    ensembleDems, ensembleReps, nDD =        0., 0., nDrawnDistricts\n",
    "    dVAP, d_whiteVAP, d_hispVAP, d_blackVAP = [0.]*nDD, [0.]*nDD, [0.]*nDD, [0.]*nDD\n",
    "    d_aianVAP, d_asianVAP, d_twoVAP =         [0.]*nDD,[0.]*nDD,[0.]*nDD\n",
    "    for d in range(nDrawnDistricts):\n",
    "        if d%1000 == 1:\n",
    "            print(\"working on HD ensemble district no\",d)\n",
    "        \n",
    "        if len(HDvtdList[d]) > 0:\n",
    "            for tt in HDvtdList[d] + partials[d]:\n",
    "                USE = 1.  #default, vtd fully used in this list\n",
    "                if tt in partials[d]:  \n",
    "                    USE = 0.   #don't double-count\n",
    "                    if tt not in HDvtdList[d]:  #this only appears in the partial list\n",
    "                        jj = partials[d].index(tt)\n",
    "                        USE = HDvtdFrac[d][jj]\n",
    "                vNo = vestNo[tt]  #must apply mapping since ensemble vtd no's in different order\n",
    "                districtDemVotes[d] += USE*unitDems[vNo]\n",
    "                districtRepVotes[d] += USE*unitReps[vNo]\n",
    "                dVAP[d]             += USE*     VAP[vNo] \n",
    "                d_whiteVAP[d]          += USE*whiteVAP[vNo]\n",
    "                d_hispVAP[d]           += USE* hispVAP[vNo]                \n",
    "                d_blackVAP[d]          += USE*blackVAP[vNo]\n",
    "                d_aianVAP[d]           += USE* aianVAP[vNo]\n",
    "                d_asianVAP[d]          += USE*asianVAP[vNo]\n",
    "                d_twoVAP[d]            += USE*  twoVAP[vNo]\n",
    "                ensembleDems        += USE*unitDems[vNo] * nStateDistricts*HDwt[d]\n",
    "                ensembleReps        += USE*unitReps[vNo] * nStateDistricts*HDwt[d]  \n",
    "                \n",
    "            districtLean[d] = districtRepVotes[d] / (districtDemVotes[d] + districtRepVotes[d] ) #done earlier\n",
    "            districtVoteMargin[d] = districtRepVotes[d] - districtDemVotes[d]\n",
    "            #district_mVAP[d]      = 1. - d_whiteVAP[d]/float(dVAP[d]) #not needed; can impute from white and total\n",
    "    print(\"total ensemble Dems and Reps, ensemble state lean are\",int(ensembleDems),int(ensembleReps),\n",
    "          r5(ensembleReps/float(ensembleReps+ensembleDems)) )\n",
    "    \n",
    "    nCutDistricts = nDrawnDistricts - nVTDs\n",
    "    vtdVAP =     [VAP[vestNo[tt]] for tt in range(nVTDs)]      + [0]*nCutDistricts\n",
    "    vtd_white =  [whiteVAP[vestNo[tt]] for tt in range(nVTDs)] + [0]*nCutDistricts\n",
    "    vtd_hisp =   [hispVAP[vestNo[tt]] for tt in range(nVTDs)]  + [0]*nCutDistricts\n",
    "    vtd_black = [blackVAP[vestNo[tt]] for tt in range(nVTDs)]  + [0]*nCutDistricts\n",
    "    vtd_aian  = [aianVAP[vestNo[tt]] for tt in range(nVTDs)]   + [0]*nCutDistricts\n",
    "    vtd_asian = [asianVAP[vestNo[tt]] for tt in range(nVTDs)]  + [0]*nCutDistricts\n",
    "    vtd_two = [twoVAP[vestNo[tt]] for tt in range(nVTDs)]      + [0]*nCutDistricts\n",
    "    vtdDems = [unitDems[vestNo[tt]] for tt in range(nVTDs)]      + [0]*nCutDistricts\n",
    "    vtdReps = [unitReps[vestNo[tt]] for tt in range(nVTDs)]      + [0]*nCutDistricts\n",
    "    new_data = {\"recalcLean\":districtLean,'voteMargin': districtVoteMargin, \"vap\":dVAP, \"vap_white\":d_whiteVAP,\n",
    "                \"vap_hisp\":d_hispVAP,\"vap_black\":d_blackVAP,\"vap_aian\":d_aianVAP,\"vap_asian\":d_asianVAP,\"vap_two\":d_twoVAP,\n",
    "               \"vtdVAP\":vtdVAP,\"vtd_white\":vtd_white,\"vtd_hisp\":vtd_hisp,\"vtd_black\":vtd_black,\n",
    "               \"vtd_aian\":vtd_aian,\"vtd_asian\":vtd_asian,\"vtd_two\":vtd_two,\"vtdDems\":vtdDems,\"vtdReps\":vtdReps}\n",
    "    outDF = inDF.assign(**new_data)\n",
    "    outPath = \"./2024state_HD_output/\"+STATE+\"_HDstats2.csv\"\n",
    "    outDF.to_csv(outPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2bd9c66-c3f1-422e-856c-546ab166fa26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AZ has 1.26608 competitive districts out of 9\n",
      "CA has 6.08415 competitive districts out of 52\n",
      "CO has 1.18979 competitive districts out of 8\n",
      "FL has 3.83315 competitive districts out of 28\n",
      "GA has 1.01858 competitive districts out of 14\n",
      "IL has 1.20658 competitive districts out of 17\n",
      "IN has 0.72028 competitive districts out of 9\n",
      "MA has 0.0 competitive districts out of 9\n",
      "MD has 0.6894 competitive districts out of 8\n",
      "MI has 2.19233 competitive districts out of 13\n",
      "MN has 0.83818 competitive districts out of 8\n",
      "MO has 0.64511 competitive districts out of 8\n",
      "NC has 2.73464 competitive districts out of 14\n",
      "NJ has 1.55092 competitive districts out of 12\n",
      "NY has 4.38999 competitive districts out of 26\n",
      "OH has 2.36449 competitive districts out of 15\n",
      "PA has 1.98348 competitive districts out of 17\n",
      "TN has 0.42106 competitive districts out of 9\n",
      "TX has 3.51829 competitive districts out of 38\n",
      "VA has 1.38592 competitive districts out of 11\n",
      "WA has 1.35263 competitive districts out of 10\n",
      "WI has 0.56413 competitive districts out of 8\n",
      "Nationally, we have 39.94916 competitive districts out of 343\n",
      "The fraction of competitive districts is 0.11646985463645439\n"
     ]
    }
   ],
   "source": [
    "#COMPUTING NUMBER OF \"COMPETITIVE DISTRICTS\"\n",
    "stateList = [\"az\",\"ca\",\"co\",\"fl\",\"ga\",\"il\",\"in\",\"ma\",\"md\",\"mi\",\"mn\",\"mo\",\"nc\",\"nj\",\"ny\",\"oh\",\"pa\",\"tn\",\"tx\",\"va\",\"wa\",\"wi\"]\n",
    "nD = [9,52,8,28,14,17,9,9,8,13,8,8,14,12,26,15,17,9,38,11,10,8]\n",
    "competitiveLean = 0.025 #i.e. how far from a tossup is a district still 'competitive'\n",
    "nStateCompetitiveDistricts = [0.]*len(nD)\n",
    "for kk,state in enumerate(stateList):\n",
    "    STATE = state.upper()\n",
    "    infile = \"./2024state_HD_output/\"+STATE+\"_HDstats2.csv\"\n",
    "    inDF = pd.read_csv(infile)\n",
    "    HDlean = inDF[\"GOPlean\"]\n",
    "    HDweight = inDF[\"HDweight\"]\n",
    "    nHDs = len(HDlean)\n",
    "    for d, lean in enumerate(HDlean):\n",
    "        if abs(lean-0.5) <= competitiveLean :\n",
    "            nStateCompetitiveDistricts[kk] += nD[kk]*HDweight[d]\n",
    "    print(STATE,\"has\",r5(nStateCompetitiveDistricts[kk]),\"competitive districts out of\",nD[kk])\n",
    "print(\"Nationally, we have\",r5(np.sum(nStateCompetitiveDistricts)),\"competitive districts out of\",np.sum(nD) )\n",
    "print(\"The fraction of competitive districts is\",np.sum(nStateCompetitiveDistricts)/np.sum(nD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d31f7805-6458-4f33-82dd-351c8fd3438a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c12b4ce-12ea-495d-9452-4fa30ac8cf17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac82551-1f34-442a-8daa-492c6ef7a56a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BELOW BLOCKS -- VOTES-TO-SEATS FITTING AND PLOTTING ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6abca7ba-6542-49f7-9866-75fb92249a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Also in this ipynb - fit the national votes-to-seats to power law + seats bias\n",
      "First, we need to compute the Trump-Clinton 2party vote share; already done for Trump-Biden\n",
      "az 0.5189\n",
      "ca 0.33876\n",
      "co 0.47317\n",
      "fl 0.50633\n",
      "ga 0.52661\n",
      "il 0.4098\n",
      "in 0.60116\n",
      "ma 0.35349\n",
      "md 0.35983\n",
      "mi 0.50118\n",
      "mn 0.49168\n",
      "mo 0.59818\n",
      "nc 0.51903\n",
      "nj 0.42716\n",
      "ny 0.38357\n",
      "oh 0.54281\n",
      "pa 0.5038\n",
      "tn 0.63624\n",
      "tx 0.54712\n",
      "va 0.47174\n",
      "wa 0.41216\n",
      "wi 0.50409\n"
     ]
    }
   ],
   "source": [
    "print(\"Also in this ipynb - fit the national votes-to-seats to power law + seats bias\")\n",
    "print(\"First, we need to compute the Trump-Clinton 2party vote share; already done for Trump-Biden\")\n",
    "\n",
    "\n",
    "    inName = \"./state_map_files/\"+state+\"_2020_vtd.csv\"\n",
    "    if state == \"ca\": \n",
    "        inName = \"./state_map_files/\"+state+\"_2020_block.csv\"\n",
    "    inDF = pd.read_csv(inName)\n",
    "    GOPvotes = inDF[\"pre_16_rep_tru\"]\n",
    "    DemVotes = inDF[\"pre_16_dem_cli\"]\n",
    "    lean = np.sum(GOPvotes)/float(np.sum(GOPvotes)+np.sum(DemVotes) )\n",
    "    print(state,r5(lean))\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f1fdcf3-f4ae-4f26-8002-fdaa8eafb381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read in the statewide votes and HD-expected seats\n"
     ]
    }
   ],
   "source": [
    "print(\"read in the statewide votes and HD-expected seats\")\n",
    "masterDF = pd.read_csv(\"./2024state_HD_output/HDmasterTable.csv\")\n",
    "STATES = masterDF[\"state\"]\n",
    "nStates = 22  #there may be extra rows in this file\n",
    "TrumpBiden = masterDF[\"true T-B\"]\n",
    "TrumpClinton = masterDF[\"true T-C\"]\n",
    "VOTES = [(TrumpBiden[i]+TrumpClinton[i])/2. for i in range(nStates)]\n",
    "SEATS = masterDF[\"seatFracExp\"][:nStates]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5689afab-5d36-4c60-8c99-4573498688c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets plot the error in the fit as a function of fixed power-law exponent for both formulations\n",
      "Now let me plot the fit biases\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let me plot the mean square error per data point by fit type\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table of exponent, fit seatsB and error, fit votesB and error\n",
      "2.5 0.00773 0.00282    0.01183 0.00223\n",
      "2.55 0.00888 0.00274    0.01195 0.00216\n",
      "2.6 0.01001 0.00266    0.01207 0.0021\n",
      "2.65 0.01111 0.0026    0.01217 0.00206\n",
      "2.7 0.0122 0.00254    0.01227 0.00204\n",
      "2.75 0.01326 0.0025    0.01236 0.00204\n",
      "2.8 0.0143 0.00246    0.01244 0.00205\n",
      "2.85 0.01532 0.00243    0.01251 0.00207\n",
      "2.9 0.01632 0.00241    0.01258 0.00211\n",
      "2.95 0.01729 0.00239    0.01264 0.00217\n",
      "3.0 0.01825 0.00239    0.01269 0.00223\n",
      "3.05 0.01915 0.00239    0.01274 0.00232\n",
      "3.1 0.02007 0.00239    0.01278 0.00241\n",
      "3.15 0.02097 0.0024    0.01282 0.00251\n",
      "3.2 0.02185 0.00242    0.01285 0.00263\n",
      "3.25 0.02271 0.00245    0.01288 0.00276\n",
      "3.3 0.02355 0.00247    0.0129 0.00289\n",
      "3.35 0.02381 0.00251    0.01291 0.00304\n",
      "3.4 0.02381 0.00255    0.01292 0.0032\n",
      "3.45 0.02381 0.0026    0.01293 0.00337\n",
      "3.5 0.02381 0.00266    0.01293 0.00354\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets plot the error in the fit as a function of fixed power-law exponent for both formulations\")\n",
    "nFits = 21\n",
    "guessedExponent = 2.7 #float( input(\"enter the true responsiveness; e.g. 2.2 \") )\n",
    "guessedSeatsBias = 0.02 #trueSB = float( input(\"enter the true seatsBias; e.g. 0.04 \") )\n",
    "setExponent = [2.5 + 0.05*i for i in range(nFits)]\n",
    "fitSeatsBias, fitVotesBias, sumError, sumError2 = list(), list(), list(), list()\n",
    "for i in range(nFits):    \n",
    "    guessedExponent = 3.1\n",
    "    guessedVotesBias = 0.012\n",
    "    root = minimize(votes2SeatsError,[guessedExponent, guessedSeatsBias],args=(VOTES, SEATS),\n",
    "                   bounds = ((setExponent[i]-.001,setExponent[i]+0.001), (-0.1,0.1)) )\n",
    "    estmdExponent = root.x[0]\n",
    "    estmdRatioBias = root.x[1]\n",
    "    fitSeats = votes2Seats([estmdExponent, estmdRatioBias], VOTES)\n",
    "    estmdSeatsBias = (1. + estmdRatioBias)/(2.+estmdRatioBias) - 0.5\n",
    "    #print(\"The fit values of the power law exponent and the seats bias are\",r5(estmdExponent),r5(estmdSeatsBias) )\n",
    "    fitSeatsBias.append(estmdSeatsBias)\n",
    "    sumError.append( np.sum([ (SEATS[n] - fitSeats[n])**2 for n in range(nStates) ]) / nFits )\n",
    "    \n",
    "\n",
    "    root = minimize(votes2SeatsError2,[guessedExponent, guessedVotesBias],args=(VOTES, SEATS),\n",
    "                   bounds = ((setExponent[i]-.001,setExponent[i]+0.001), (-0.1,0.1)) )\n",
    "    estmdExponent2 = root.x[0]\n",
    "    estmdVotesBias = root.x[1]\n",
    "    fitSeats2 = votes2Seats2([estmdExponent2, estmdVotesBias], VOTES)\n",
    "    #print(\"The fit values of the power law exponent and the votesbias are\",r5(estmdExponent2),r5(estmdVotesBias) )\n",
    "    fitVotesBias.append(estmdVotesBias)\n",
    "    sumError2.append( np.sum([ (SEATS[n] - fitSeats2[n])**2 for n in range(nStates) ]) / nFits)\n",
    "print(\"Now let me plot the fit biases\")\n",
    "plt.scatter(setExponent,fitSeatsBias,label=\"fit seats bias\")\n",
    "plt.scatter(setExponent,fitVotesBias,label=\"fit votes bias\")\n",
    "plt.xlabel(\"assumed power law exponent\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"Now let me plot the mean square error per data point by fit type\")\n",
    "plt.scatter(setExponent,sumError,label=\"MSE using seats fit\")\n",
    "plt.scatter(setExponent,sumError2,label=\"MSE using votes fit\")\n",
    "plt.xlabel(\"assumed power law exponent\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"table of exponent, fit seatsB and error, fit votesB and error\")\n",
    "for i in range(nFits):\n",
    "    print(setExponent[i],r5(fitSeatsBias[i]),r5(sumError[i]),\"  \",r5(fitVotesBias[i]),r5(sumError2[i]) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab532586-ebc3-46a8-9c4b-fceb227397d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will do two final fits:  A has seats ratio = bias + votesRatio^n   B has seats ratio = biasedVotesRatio^n\n",
      "The fit values of the power law exponent and the seats bias are 3.02892 0.01878\n",
      "The fit values of the power law exponent and the votesbias are 2.74042 0.01234\n",
      "Here are the curve fits\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "guessedExponent = 2.7 #float( input(\"enter the true responsiveness; e.g. 2.2 \") )\n",
    "guessedSeatsBias = 0.02 #trueSB = float( input(\"enter the true seatsBias; e.g. 0.04 \") )\n",
    "print(\"We will do two final fits:  A has seats ratio = bias + votesRatio^n   B has seats ratio = biasedVotesRatio^n\")\n",
    "\n",
    "root = minimize(votes2SeatsError,[guessedExponent, guessedSeatsBias],args=(VOTES, SEATS))\n",
    "estmdExponent = root.x[0]\n",
    "estmdRatioBias = root.x[1]\n",
    "fitSeats = votes2Seats([estmdExponent, estmdRatioBias], VOTES)\n",
    "estmdSeatsBias = (1. + estmdRatioBias)/(2.+estmdRatioBias) - 0.5\n",
    "print(\"The fit values of the power law exponent and the seats bias are\",r5(estmdExponent),r5(estmdSeatsBias) )\n",
    "\n",
    "guessedExponent = 3.1\n",
    "guessedVotesBias = 0.012\n",
    "root = minimize(votes2SeatsError2,[guessedExponent, guessedVotesBias],args=(VOTES, SEATS))\n",
    "estmdExponent2 = root.x[0]\n",
    "estmdVotesBias = root.x[1]\n",
    "fitSeats2 = votes2Seats2([estmdExponent2, estmdVotesBias], VOTES)\n",
    "print(\"The fit values of the power law exponent and the votesbias are\",r5(estmdExponent2),r5(estmdVotesBias) )\n",
    "\n",
    "print(\"Here are the curve fits\")\n",
    "for i in range(nStates):\n",
    "    xShift = 0.\n",
    "    plt.scatter(VOTES[i],SEATS[i],marker=\"*\")\n",
    "    shift = 0.07 * np.sign(0.5 - SEATS[i])\n",
    "    if STATES[i] in [\"CA\",\"GA\",\"IN\",\"MA\",\"NJ\",\"OH\",\"PA\",\"NC\",\"WA\"]:\n",
    "        shift *= -1\n",
    "    plt.text(VOTES[i]+xShift, SEATS[i] + shift ,STATES[i],color=\"black\",ha=\"center\",va=\"center\")\n",
    "\n",
    "fitV = [0.34 + 0.0251*i for i in range(12)]\n",
    "fitS = [votes2Seats([estmdExponent, estmdRatioBias], [fitV[i]])[0]  for i in range(12)]\n",
    "plt.plot(fitV, fitS,linestyle=\"--\")\n",
    "\n",
    "fitV = [0.34 + 0.0251*i for i in range(12)]\n",
    "fitS2 = [votes2Seats2([estmdExponent2, estmdVotesBias], [fitV[i]])[0]  for i in range(12)]\n",
    "plt.plot(fitV, fitS2,linestyle=\"--\",color=\"black\",lw=0.5)\n",
    "#plt.scatter(VOTES, SEATS)\n",
    "idx = np.argsort(VOTES)\n",
    "plt.plot([VOTES[idx[i]] for i in range(nStates) ],[fitSeats[idx[i]] for i in range(nStates)],linestyle=\"--\")\n",
    "plt.xlabel(\"2016-2020 avg 2party GOP vote share\")\n",
    "plt.ylabel(\"GOP seat fraction\")\n",
    "plt.ylim(0.)\n",
    "plt.axhline(0.5,lw=0.2,color=\"black\" )\n",
    "plt.axhline(0.5+estmdSeatsBias,lw=0.5,dashes=(10,10),color=\"gray\")\n",
    "plt.axvline(0.5,lw=0.2,color=\"black\")\n",
    "plt.axvline(0.5-estmdVotesBias,lw=0.5,dashes=(5,10),color=\"gray\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a88587aa-d1eb-4ba7-addf-c1726461d237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e956a88e-e97b-46e1-81bc-7fb5a021aadb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We now read in the Kenny quantiles\n"
     ]
    }
   ],
   "source": [
    "print(\"We now read in the Kenny quantiles\")\n",
    "Kenny025 = masterDF[\"025seatFrac\"]\n",
    "Kenny17 = masterDF[\"17seatFrac\"]\n",
    "Kenny50 = masterDF[\"50seatFrac\"]\n",
    "Kenny83 = masterDF[\"83seatFrac\"]\n",
    "Kenny975 = masterDF[\"975seatFrac\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2498f9a5-b3c2-4f4a-a7f4-3887a9171c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below is disproportionality\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Below is disproportionality\")\n",
    "for i in range(nStates):\n",
    "    xW0, xW1 = 0.005, 0.003\n",
    "    xShift, yShift = 0.01,0.\n",
    "    yActual = SEATS[i] - VOTES[i]\n",
    "    plt.plot(VOTES[i],yActual,marker=\"*\",markerfacecolor=\"none\")\n",
    "    if STATES[i] in [\"CO\",\"PA\",\"MD\",\"MI\",\"MN\",\"NY\", \"OH\",\"WA\",\"WI\"]:\n",
    "        xShift, yShift = -0.01, 0\n",
    "    if STATES[i] in [\"IL\"]:\n",
    "        yShift += 0.005\n",
    "        xShift -= 0.003\n",
    "    if STATES[i] in [\"NC\"]:\n",
    "        xShift += 0.0058\n",
    "        yShift -= 0.0017\n",
    "    if STATES[i] in [\"CA\"]:\n",
    "        xShift -= 0.01\n",
    "        yShift -= 0.035\n",
    "    if STATES[i] in [\"CA\",\"NC\"]:\n",
    "        xW0, xW1 = 0.0062, 0.004\n",
    "    plt.text(VOTES[i]+xShift, yActual + yShift ,STATES[i],color=\"black\",ha=\"center\",va=\"center\")\n",
    "    pt0  = Point(VOTES[i] + xW1, Kenny025[i] - VOTES[i])\n",
    "    pt1  = Point(VOTES[i] + xW1, Kenny17[i] -  VOTES[i])\n",
    "    pt2  = Point(VOTES[i] + xW0, Kenny17[i] -  VOTES[i])\n",
    "    pt3  = Point(VOTES[i] + xW0, Kenny83[i] -  VOTES[i])\n",
    "    pt4  = Point(VOTES[i] + xW1, Kenny83[i] -  VOTES[i])\n",
    "    pt5  = Point(VOTES[i] + xW1, Kenny975[i] - VOTES[i])\n",
    "    pt11 = Point(VOTES[i] - xW1, Kenny025[i] - VOTES[i])\n",
    "    pt10 = Point(VOTES[i] - xW1, Kenny17[i] -  VOTES[i])\n",
    "    pt9  = Point(VOTES[i] - xW0, Kenny17[i] -  VOTES[i])\n",
    "    pt8  = Point(VOTES[i] - xW0, Kenny83[i] -  VOTES[i])\n",
    "    pt7  = Point(VOTES[i] - xW1, Kenny83[i] -  VOTES[i])\n",
    "    pt6  = Point(VOTES[i] - xW1, Kenny975[i] - VOTES[i])\n",
    "    KennyPolly = Polygon([pt0,pt1,pt2,pt3,pt4,pt5,pt6,pt7,pt8,pt9, pt10, pt11])\n",
    "    LINEWIDTH, LS = 0.2, \"-\"\n",
    "    if STATES[i] == \"CA\" or STATES[i] == \"NC\":\n",
    "        #KennyPolly = translate(KennyPolly,xoff=-0.01,yoff=0)\n",
    "        LINEWIDTH, LS = 1.4, \"dotted\"\n",
    "        xDELT, yDELT = 0.008, 0.012\n",
    "        boxXs = [ VOTES[i]+xShift - xDELT,  VOTES[i]+xShift + xDELT]\n",
    "        boxYs = [yActual + yShift - yDELT, yActual + yShift + yDELT]\n",
    "        POINTS = [Point(boxXs[0],boxYs[0]),Point(boxXs[1],boxYs[0]),Point(boxXs[1],boxYs[1]),Point(boxXs[0],boxYs[1]) ]\n",
    "        boxPoly = Polygon(POINTS)\n",
    "        plotPolyColorWidthStyle(boxPoly,\"black\",LINEWIDTH,\"dotted\")\n",
    "    plotPolyColorWidthStyle( KennyPolly,\"black\",LINEWIDTH,LS)\n",
    "    #plt.text(VOTES[i],Kenny50[i]-VOTES[i],\".\",fontsize=12,ha=\"center\")  #too busy with median plotted\n",
    "\n",
    "#plt.scatter(VOTES, SEATS)\n",
    "idx = np.argsort(VOTES)\n",
    "nPlot = 12\n",
    "fitV = [0.35 + 0.025*i for i in range(nPlot)]\n",
    "fitDisp = [ votes2Seats2([estmdExponent2, estmdVotesBias], fitV)[i] - fitV[i] for i in range(nPlot) ]\n",
    "plt.plot(fitV, fitDisp,linestyle=\"--\")\n",
    "plt.xlabel(\"GOP vote share\")  #2016-2020 avg 2party GOP vote share\n",
    "plt.ylabel(\"expected disproportionality\")  #GOP seat fraction - vote fraction\n",
    "#plt.ylim(0.3)\n",
    "plt.axhline(0.0,lw=0.4,color=\"black\" )\n",
    "#plt.axhline(0.0+estmdSeatsBias,lw=0.5,dashes=(10,10),color=\"gray\")\n",
    "#plt.axvline(0.5 - estmdVotesBias,lw=0.5,dashes=(10,10),color=\"gray\") \n",
    "#  above doesn't produce an intercept based on how we plot, so drop it\n",
    "plt.axvline(0.5,lw=0.4,color=\"black\")\n",
    "plt.plot([0.35,0.63],[-0.15,0.13],lw=0.2,color=\"black\")\n",
    "plt.text(0.57,0.05,\"0 effcy gap\",rotation=25,fontsize=14)\n",
    "plt.text(0.36,-0.185,\"Eq. 9 fit\",rotation=22,color=[0.,0.4,0.],fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d4d57ddb-c63d-4214-b621-df03e2c8eb45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7404183338305304 0.012343331366669007\n",
      "[0.5]\n"
     ]
    }
   ],
   "source": [
    "print(estmdExponent2,estmdVotesBias)\n",
    "print(votes2Seats2([estmdExponent2,estmdVotesBias],[0.5-estmdVotesBias]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b359a464-5e93-40b5-8569-33d7c213c217",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Repeating above, with MA excluded\")\n",
    "guessedExponent = 2.7 #float( input(\"enter the true responsiveness; e.g. 2.2 \") )\n",
    "guessedSeatsBias = 0.02 #trueSB = float( input(\"enter the true seatsBias; e.g. 0.04 \") )\n",
    "print(\"We will do two fits:  A has seats ratio = bias + votesRatio^n   B has seats ratio = biasedVotesRatio^n\")\n",
    "modVOTES, modSEATS = list(),list()\n",
    "for i in range(nStates):\n",
    "    if STATES[i] != \"MA\":\n",
    "        modVOTES.append(VOTES[i])\n",
    "        modSEATS.append(SEATS[i])\n",
    "\n",
    "root = minimize(votes2SeatsError,[guessedExponent, guessedSeatsBias],args=(modVOTES, modSEATS))\n",
    "estmdExponent = root.x[0]\n",
    "estmdRatioBias = root.x[1]\n",
    "fitSeats = votes2Seats([estmdExponent, estmdRatioBias], VOTES)\n",
    "estmdSeatsBias = (1. + estmdRatioBias)/(2.+estmdRatioBias) - 0.5\n",
    "print(\"Excluding MA, the exponent and the seats bias are\",r5(estmdExponent),r5(estmdSeatsBias) )\n",
    "\n",
    "guessedExponent = 3.1\n",
    "guessedVotesBias = 0.012\n",
    "root = minimize(votes2SeatsError2,[guessedExponent, guessedVotesBias],args=(modVOTES, modSEATS),\n",
    "               bounds = ((0.5,5), (-0.1, 0.1)) )\n",
    "estmdExponent2 = root.x[0]\n",
    "estmdVotesBias = root.x[1]\n",
    "fitSeats2 = votes2Seats2([estmdExponent2, estmdVotesBias], VOTES)\n",
    "print(\"Excluding MA, the exponent and the votesbias are\",r5(estmdExponent2),r5(estmdVotesBias) )\n",
    "\n",
    "print(\"Here are the curve fits\")\n",
    "for i in range(nStates):\n",
    "    xShift = 0.\n",
    "    plt.scatter(VOTES[i],SEATS[i],marker=\"*\")\n",
    "    shift = 0.07 * np.sign(0.5 - SEATS[i])\n",
    "    if STATES[i] in [\"CA\",\"GA\",\"IN\",\"MA\",\"NJ\",\"OH\",\"PA\",\"NC\",\"WA\"]:\n",
    "        shift *= -1\n",
    "    plt.text(VOTES[i]+xShift, SEATS[i] + shift ,STATES[i],color=\"black\",ha=\"center\",va=\"center\")\n",
    "\n",
    "fitV = [0.34 + 0.0251*i for i in range(12)]\n",
    "fitS = [votes2Seats([estmdExponent, estmdRatioBias], [fitV[i]])[0]  for i in range(12)]\n",
    "plt.plot(fitV, fitS,linestyle=\"--\")\n",
    "\n",
    "fitV = [0.34 + 0.0251*i for i in range(12)]\n",
    "fitS2 = [votes2Seats2([estmdExponent2, estmdVotesBias], [fitV[i]])[0]  for i in range(12)]\n",
    "plt.plot(fitV, fitS2,linestyle=\"--\",color=\"black\",lw=0.5)\n",
    "#plt.scatter(VOTES, SEATS)\n",
    "idx = np.argsort(VOTES)\n",
    "plt.plot([VOTES[idx[i]] for i in range(nStates) ],[fitSeats[idx[i]] for i in range(nStates)],linestyle=\"--\")\n",
    "plt.xlabel(\"2016-2020 avg 2party GOP vote share\")\n",
    "plt.ylabel(\"GOP seat fraction\")\n",
    "plt.ylim(0.)\n",
    "plt.axhline(0.5,lw=0.2,color=\"black\" )\n",
    "plt.axhline(0.5+estmdSeatsBias,lw=0.5,dashes=(10,10),color=\"gray\")\n",
    "plt.axvline(0.5,lw=0.2,color=\"black\")\n",
    "plt.axvline(0.5-estmdVotesBias,lw=0.5,dashes=(5,10),color=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c88c197-2a28-4dad-9e91-cd6f38f83b30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "16c9b6ac-b9d2-45ed-9ff5-e509e35e7c6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THIS BLOCK -- PLOTTING COMPETITIVENESS\n",
      "read in the statewide votes and HD-expected seats\n",
      "I read in the master data.  There are 22 states. Here is the list of map drawers ['Comsn', 'GOP', 'Dem', 'Mixed', 'Court']\n"
     ]
    }
   ],
   "source": [
    "print(\"THIS BLOCK -- PLOTTING COMPETITIVENESS\")\n",
    "print(\"read in the statewide votes and HD-expected seats\")\n",
    "masterDF = pd.read_csv(\"./2024state_HD_output/HDmasterTable.csv\")\n",
    "STATES = masterDF[\"state\"]\n",
    "nStates = 22  #there may be extra rows in this file for states re-run without VRA compliance\n",
    "TrumpBiden = masterDF[\"true T-B\"]\n",
    "TrumpClinton = masterDF[\"true T-C\"]\n",
    "VOTES = [(TrumpBiden[i]+TrumpClinton[i])/2. for i in range(nStates)]\n",
    "enactedSeats = masterDF[\"seatFracEn\"][:nStates]\n",
    "expSeats =  masterDF[\"seatFracExp\"][:nStates]\n",
    "enactedR =  masterDF[\"Ren\"][:nStates]\n",
    "expectedR = masterDF[\"Rexp\"][:nStates]\n",
    "agent = masterDF[\"agent\"][:nStates]\n",
    "allAgents = list()\n",
    "for a in agent:\n",
    "    if a not in allAgents:\n",
    "        allAgents.append(a)\n",
    "print(\"I read in the master data.  There are\",nStates,\"states. Here is the list of map drawers\",allAgents)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "19109607-701a-4681-b459-e2428e44eac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nAgents = len(allAgents)\n",
    "aSymbol = [\"*\",\">\",\"<\",\"o\",\"P\" ]\n",
    "aColor = [\"green\",\"red\",\"blue\",\"purple\",\"black\"]\n",
    "for i in range(nStates):\n",
    "    agentNo = allAgents.index(agent[i])\n",
    "    plt.scatter(expectedR[i],enactedR[i],marker=aSymbol[agentNo],color=aColor[agentNo])\n",
    "    xShift,yShift = 0.,0.09\n",
    "    if STATES[i] in [\"MD\",\"MN\",\"NJ\",\"WI\",\"WA\",\"AZ\"]:\n",
    "        yShift = -0.15\n",
    "    if STATES[i] in [\"MI\"]:\n",
    "        xShift, yShift = -0.1,-0.02\n",
    "    plt.text(expectedR[i]+xShift,enactedR[i]+yShift,STATES[i],ha=\"center\",color=aColor[agentNo])\n",
    "    plt.plot([0.5,3.5],[0.5,3.5],ls=\"-\",lw=0.4,color=\"black\",dashes = (8,6))\n",
    "xStart, yStart, xDelta, yDelta = 0.5, 3.5, 0.1, -0.2\n",
    "for i in range(nAgents):\n",
    "    plt.scatter(xStart, yStart + yDelta*i,marker=aSymbol[i],color=aColor[i])\n",
    "    plt.text(xStart+xDelta, yStart + yDelta*i - 0.08, allAgents[i],color=aColor[i])\n",
    "plt.text(1.5,1.63,\"y = x\",rotation=35)\n",
    "plt.xlabel(\"expected responsiveness\")\n",
    "plt.ylabel(\"enacted responsiveness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "26baa05d-aef9-45b3-a422-d2a1e18d794f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 435 total U.S. House districts.  Our analysis covers 343.0 =  0.78851\n",
      "The national average responsiveness (enacted and expected) are 1.2168 1.5959\n",
      "0.7624570982413504 is the enacted/expected competitiveness\n"
     ]
    }
   ],
   "source": [
    "nationalEnactedR, nationalExpectedR = 0., 0.\n",
    "nDistricts = masterDF[\"nDistricts\"][:nStates]\n",
    "nationalNdistricts = np.sum(nDistricts)\n",
    "print(\"There are 435 total U.S. House districts.  Our analysis covers\",nationalNdistricts,\"= \",r5(nationalNdistricts/435.))\n",
    "for i in range(nStates):\n",
    "    nationalNdistricts += nDistricts[i]\n",
    "    nationalEnactedR +=  nDistricts[i] * enactedR[i]  / float(nationalNdistricts)\n",
    "    nationalExpectedR += nDistricts[i] * expectedR[i] / float(nationalNdistricts)\n",
    "print(\"The national average responsiveness (enacted and expected) are\",r5(nationalEnactedR),r5(nationalExpectedR) )\n",
    "print(nationalEnactedR/nationalExpectedR,\"is the enacted/expected competitiveness\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdf2cc3-2339-464d-9c17-9d45d7f2edf4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34097cd2-5b77-4d33-bdba-3af91b0137c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, lets parse out Cincy vs. Cleveland VRA impacts\n",
      "We will read in HDtractLists for with and without VRA enforcement\n",
      "Then we will compute seat expectations for each Home District\n",
      " ... and sum the VRA vs. nonVRA expectations by county\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the two-letter state code; e.g. MN OH\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code computes partisan and VRA metrics of an ensemble of districts, where each district is a collection of units\n",
      "No spatial analysis here, just district metrics for HD drawing and McCartan 50-state sims vs. enacted\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the number of Congressional districts; e.g. 8 for MN 15\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's read in the 2020 and 2016 voting data for OH mapped to 2020 vtds\n",
      "Note, on 2-10-24, I combined 2016 and 2020 data to a single file for easier use\n",
      "In year/election 2020 Dem and GOP total votes were 2678283 3154724 for a stateGOP of 0.54084\n",
      "There are a total of 8941  vote-mapped voting districts from election year 2020 in state OH\n",
      "In year/election 2016 Dem and GOP total votes were 2392589 2840665 for a stateGOP of 0.54281\n",
      "There are a total of 8941  vote-mapped voting districts from election year 2016 in state OH\n",
      "Now ready to compute each HD's partisan stats.  Votes are based on order in vest20 file.\n",
      "First, we collapse all precinct election results into avg Dem and Rep vote totals per precinct, following Kenny\n",
      "We follow Kenny 2023 in assigning partisan votes based on arithmetic avg 2-party vote share * logmean total turnout\n",
      "All done averaging over elections. The composite statewide vote margin = 468339 with stateGOP ~ 0.54251 vs arithm average 0.54183\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, lets parse out Cincy vs. Cleveland VRA impacts\")\n",
    "print(\"We will read in HDtractLists for with and without VRA enforcement\")\n",
    "print(\"Then we will compute seat expectations for each Home District\")\n",
    "print(\" ... and sum the VRA vs. nonVRA expectations by county\")\n",
    "\n",
    "STATE = input(\"enter the two-letter state code; e.g. MN\")\n",
    "print(\"this code computes partisan and VRA metrics of an ensemble of districts, where each district is a collection of units\")\n",
    "print(\"No spatial analysis here, just district metrics for HD drawing and McCartan 50-state sims vs. enacted\")\n",
    "nStateDistricts = int(input(\"enter the number of Congressional districts; e.g. 8 for MN\"))\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE,\"mapped to 2020 vtds\")\n",
    "print(\"Note, on 2-10-24, I combined 2016 and 2020 data to a single file for easier use\")\n",
    "DemCandidates, RepCandidates= [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ]\n",
    "years = [str(2020),str(2016)]\n",
    "nYears = len(years)\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "if STATE == \"CA\":\n",
    "    vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_t\"\n",
    "else:\n",
    "    vestName = \"./state_map_files/\" + STATE.lower()+\"_2020_vtd\"\n",
    "vestDF = pd.read_csv(vestName+\".csv\")  \n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = vestDF[DemCandidates[y]].copy(), vestDF[RepCandidates[y]].copy(), vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election year\",years[y],\"in state\",STATE)\n",
    "\n",
    "print(\"Now ready to compute each HD's partisan stats.  Votes are based on order in vest20 file.\")\n",
    "print(\"First, we collapse all precinct election results into avg Dem and Rep vote totals per precinct, following Kenny\")\n",
    "print(\"We follow Kenny 2023 in assigning partisan votes based on arithmetic avg 2-party vote share * logmean total turnout\")\n",
    "unitVotes = [ ((DemVotes[0][v] + RepVotes[0][v]) * (DemVotes[1][v] + RepVotes[1][v]) ) ** 0.5 for v in range(nVTDs) ]\n",
    "unitDemFrac = [ 0.5 * ( DemVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                        DemVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "unitRepFrac = [ 0.5 * ( RepVotes[0][v] / max( DemVotes[0][v] + RepVotes[0][v],0.001) +  \n",
    "                        RepVotes[1][v] / max( DemVotes[1][v] + RepVotes[1][v],0.001) )  for v in range(nVTDs) ]\n",
    "unitLean = [1. - unitDemFrac[v] for v in range(nVTDs) ]   #above -- prevent divide by zero\n",
    "for v in range(nVTDs):\n",
    "    if (unitVotes[v] < 2):\n",
    "        unitLean[v] = 0.5  #set all low-vote precincts to tossups\n",
    "        \n",
    "unitDems = [ unitDemFrac[v] * unitVotes[v] for v in range(nVTDs) ]  #Now consolidating elections to single partisan measure by precinct\n",
    "unitReps = [ unitRepFrac[v] * unitVotes[v] for v in range(nVTDs) ]\n",
    "stateDems, stateReps = np.sum(unitDems), np.sum(unitReps)\n",
    "stateMargin = stateReps - stateDems\n",
    "stateLean = stateReps / (stateReps + stateDems)\n",
    "avgStateVotes = ((np.sum(DemVotes[0])+np.sum(RepVotes[0])) * (np.sum(DemVotes[1])+np.sum(RepVotes[1]) ) )**0.5\n",
    "print(\"All done averaging over elections. The composite statewide vote margin =\",int(stateMargin),\n",
    "      \"with stateGOP ~\", r5(stateLean),\"vs arithm average\", r5(np.average(yearLean)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34b8fa9d-8674-43bd-8670-032e861cda40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for OH\n",
      "Here, we read in 2 files -- nonVRA and with VRA\n",
      "This ensemble map describes 8941 drawn districts\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "Now reading in HD vtd lists from second file (no VRA enforcement), same HD order\n",
      "The second ensemble map describes 8941 drawn districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "print(\"Here, we read in 2 files -- nonVRA and with VRA\")\n",
    "\n",
    "infilename = \"OH8941VRApatchedVTDs.csv\"\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nDrawnDistricts = nRows\n",
    "print(\"This ensemble map describes\",nRows,\"drawn districts\")\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDwt = HDdf['HDweight']  #'HDwt'\n",
    "HDweight = HDwt.copy()  #nomenclature\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "if \"splitTractNo\" in HDdf.columns.values:  #this tract list came direct from polygon capture\n",
    "    HDvtdListString = HDdf['HDvtdList']\n",
    "    splitTractNo, splitTractUse = HDdf['splitTractNo'], HDdf['splitTractUse']\n",
    "    partials = [ list() for i in splitTractNo ]  #default, no partials\n",
    "    for i,t in enumerate(splitTractNo):\n",
    "        if t >= 0:  #i.e. not the -999 flag for no partials\n",
    "            partials[i] = [t]\n",
    "else :  #the HD tract lists come from contig patching\n",
    "    splitTractNo, splitTractUse = [-999 for t in range(nRows)] , [0. for t in range(nRows)]\n",
    "    HDvtdListString = HDdf['HDvtdList']\n",
    "    HDvtdList = [ast.literal_eval(L) for L in HDvtdListString]\n",
    "    if \"partials\" in HDdf.columns:\n",
    "        partialsString =  HDdf['partials']\n",
    "        partials = [ast.literal_eval(L) for L in partialsString]\n",
    "        HDvtdFracString = HDdf['HDvtdFrac']\n",
    "        HDvtdFrac = [ast.literal_eval(L) for L in HDvtdFracString]\n",
    "    else:\n",
    "        print(STATE,\"had no partial VTD column; only whole VTDs used to build HDs\")\n",
    "        partials = [list() for v in range(nRows)]\n",
    "        HDvtdFrac = [list() for v in range(nRows)]\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDwt)) )\n",
    "print(\"Now reading in HD vtd lists from second file (no VRA enforcement), same HD order\")\n",
    "infilename2 = \"OH8941redoPatchedVTDs.csv\"\n",
    "HDdf2 = pd.read_csv(\"2024state_HD_output/\"+infilename2)\n",
    "nRows2 = len(HDdf2)\n",
    "nDrawnDistricts2 = nRows2\n",
    "print(\"The second ensemble map describes\",nRows2,\"drawn districts\")\n",
    "HDvPop2 = HDdf2[\"HDvPop\"].to_list()\n",
    "#tractCPx = HDdf['centroid x'].to_list()  #assume in same order as first DF\n",
    "#tractCPy = HDdf['centroid y'].to_list()\n",
    "if \"splitTractNo\" in HDdf2.columns.values:  #this tract list came direct from polygon capture\n",
    "    HDvtdListString2 = HDdf2['HDvtdList']\n",
    "    splitTractNo2, splitTractUse2 = HDdf2['splitTractNo'], HDdf2['splitTractUse']\n",
    "    partials2 = [ list() for i in splitTractNo2 ]  #default, no partials\n",
    "    for i,t in enumerate(splitTractNo2):\n",
    "        if t >= 0:  #i.e. not the -999 flag for no partials\n",
    "            partials2[i] = [t]\n",
    "else :  #the HD tract lists come from contig patching\n",
    "    splitTractNo2, splitTractUse2 = [-999 for t in range(nRows2)] , [0. for t in range(nRows2)]\n",
    "    HDvtdListString2 = HDdf2['HDvtdList']\n",
    "    HDvtdList2 = [ast.literal_eval(L) for L in HDvtdListString2]\n",
    "    if \"partials\" in HDdf2.columns:\n",
    "        partialsString =  HDdf2['partials']\n",
    "        partials2 = [ast.literal_eval(L) for L in partialsString]\n",
    "        HDvtdFracString2 = HDdf2['HDvtdFrac']\n",
    "        HDvtdFrac2 = [ast.literal_eval(L) for L in HDvtdFracString2]\n",
    "    else:\n",
    "        print(STATE,\"had no partial VTD column; only whole VTDs used to build 2nd HD sets\")\n",
    "        partials2 = [list() for v in range(nRows2)]\n",
    "        HDvtdFrac2 = [list() for v in range(nRows2)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66150ede-c2dc-40c3-961e-67b94f5ed4a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oops, I forgot to write the GEOID20 column to the HD output file\n",
      "Need to read in the original Census VTD file to translate HD vtd no's to vest no's.  Stand by ...\n",
      "OH 's total population is 11799448\n",
      "Our VEST file had 8941 vtds. Merging with our HD list file left us with 8941 vtds\n",
      "15 8941 8941   nDistricts (enacted, total HD 1, total HD2)\n"
     ]
    }
   ],
   "source": [
    "dummyNo = [v for v in range(nVTDs)]\n",
    "targetLen = 11  #in some cases, the leading 0's in geoid's were truncated in conversion to strings\n",
    "vestLen = len(str(vestDF['GEOID20'][0]))\n",
    "addLen = targetLen - vestLen\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":[\"0\"*addLen + str(geo) for geo in vestDF['GEOID20']], \"vestNo\":dummyNo} )\n",
    "if \"GEOID20\" in HDdf.columns:  \n",
    "    print(\"I will map vtd numbers directly from the HD output file onto vest numbers ...\")\n",
    "    #HDTL specifies Census VTD's, which may be in different order from vest. Define the translation\n",
    "    print(\"translating from HD order of precincts to VEST order\")\n",
    "    mappingDF = pd.merge(HDdf,miniVestDF,on=\"GEOID20\")\n",
    "    #mappingDF2 = pd.merge(HDdf2,miniVestDF,on=\"GEOID20\")  #not needed, HD files in same vtd order\n",
    "\n",
    "else:\n",
    "    print(\"oops, I forgot to write the GEOID20 column to the HD output file\")\n",
    "    print(\"Need to read in the original Census VTD file to translate HD vtd no's to vest no's.  Stand by ...\")\n",
    "    if STATE == \"CA\":\n",
    "        popFilename = STATE.lower()+\"_pl2020_t.dbf\"\n",
    "    else:\n",
    "        popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "    tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "    tractPop = tractPopFile['P0010001']\n",
    "    statePop = np.sum(tractPop)\n",
    "    print(STATE,\"'s total population is\",statePop)\n",
    "    popLen = len(tractPopFile['GEOID20'][0])\n",
    "    addLen = targetLen - popLen\n",
    "    censusGEOID20 = [\"0\"*addLen + str(geo) for geo in tractPopFile['GEOID20'] ]\n",
    "    miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "    mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "    \n",
    "vestNo = mappingDF['vestNo']\n",
    "print(\"Our VEST file had\",len(vestDF),\"vtds. Merging with our HD list file left us with\",len(mappingDF),\"vtds\")\n",
    "print(nStateDistricts,nDrawnDistricts, nDrawnDistricts2,\n",
    "      \"  nDistricts (enacted, total HD 1, total HD2)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a95a68f4-193b-4de1-a5c8-3481ff291fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now summing Dem and Rep votes, minorities from precincts assigned to each HD row ...\n",
      "working on HD ensemble district no 1\n",
      "working on HD ensemble district no 1001\n",
      "working on HD ensemble district no 2001\n",
      "working on HD ensemble district no 3001\n",
      "working on HD ensemble district no 4001\n",
      "working on HD ensemble district no 5001\n",
      "working on HD ensemble district no 6001\n",
      "working on HD ensemble district no 7001\n",
      "working on HD ensemble district no 8001\n",
      "working on HD2 ensemble district no 1\n",
      "working on HD2 ensemble district no 1001\n",
      "working on HD2 ensemble district no 2001\n",
      "working on HD2 ensemble district no 3001\n",
      "working on HD2 ensemble district no 4001\n",
      "working on HD2 ensemble district no 5001\n",
      "working on HD2 ensemble district no 6001\n",
      "working on HD2 ensemble district no 7001\n",
      "working on HD2 ensemble district no 8001\n"
     ]
    }
   ],
   "source": [
    "print(\"Now summing Dem and Rep votes, minorities from precincts assigned to each HD row ...\")\n",
    "#note: no VAP stats in this block\n",
    "districtDemVotes, districtRepVotes = [0.]*nDrawnDistricts , [0.]*nDrawnDistricts\n",
    "districtLean, districtVoteMargin =  [0.5]*nDrawnDistricts , [0.]*nDrawnDistricts\n",
    "ensembleDems, ensembleReps =        0., 0.\n",
    "for d in range(nDrawnDistricts):\n",
    "    if d%1000 == 1:\n",
    "        print(\"working on HD ensemble district no\",d)\n",
    "    if len(HDvtdList[d]) > 0:\n",
    "        for tt in HDvtdList[d] + partials[d]:\n",
    "            USE = 1.  #default, vtd fully used in this list\n",
    "            if tt in partials[d]:  \n",
    "                USE = 0.   #don't double-count\n",
    "                if tt not in HDvtdList[d]:  #this only appears in the partial list\n",
    "                    jj = partials[d].index(tt)\n",
    "                    USE = HDvtdFrac[d][jj]\n",
    "            vNo = vestNo[tt]  #must apply mapping since ensemble vtd no's in different order\n",
    "            districtDemVotes[d] += USE*unitDems[vNo]\n",
    "            districtRepVotes[d] += USE*unitReps[vNo]\n",
    "            ensembleDems        += USE*unitDems[vNo] * nStateDistricts*HDwt[d]\n",
    "            ensembleReps        += USE*unitReps[vNo] * nStateDistricts*HDwt[d]            \n",
    "        districtLean[d] = districtRepVotes[d] / (districtDemVotes[d] + districtRepVotes[d] )\n",
    "\n",
    "districtDemVotes2, districtRepVotes2 = [0.]*nDrawnDistricts2 , [0.]*nDrawnDistricts2\n",
    "districtLean2, districtVoteMargin2 =  [0.5]*nDrawnDistricts2 , [0.]*nDrawnDistricts2\n",
    "ensembleDems2, ensembleReps2 =        0., 0.\n",
    "for d in range(nDrawnDistricts2):\n",
    "    if d%1000 == 1:\n",
    "        print(\"working on HD2 ensemble district no\",d)\n",
    "    if len(HDvtdList2[d]) > 0:\n",
    "        for tt in HDvtdList2[d] + partials2[d]:\n",
    "            USE = 1.  #default, vtd fully used in this list\n",
    "            if tt in partials2[d]:  \n",
    "                USE = 0.   #don't double-count\n",
    "                if tt not in HDvtdList2[d]:  #this only appears in the partial list\n",
    "                    jj = partials2[d].index(tt)\n",
    "                    USE = HDvtdFrac2[d][jj]\n",
    "            vNo = vestNo[tt]  #must apply mapping since ensemble vtd no's in different order\n",
    "            districtDemVotes2[d] += USE*unitDems[vNo]\n",
    "            districtRepVotes2[d] += USE*unitReps[vNo]\n",
    "            ensembleDems2        += USE*unitDems[vNo] * nStateDistricts*HDwt[d]\n",
    "            ensembleReps2        += USE*unitReps[vNo] * nStateDistricts*HDwt[d]            \n",
    "        districtLean2[d] = districtRepVotes2[d] / (districtDemVotes2[d] + districtRepVotes2[d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7acbb60c-5c0b-45fd-891a-5fe763de0514",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating partisan seat expectations for both HD ensembles...\n"
     ]
    }
   ],
   "source": [
    "print(\"Calculating partisan seat expectations for both HD ensembles...\")\n",
    "nu, nuSigma = 3.8, 0.221  #see Imai-logit.ipynb for how we fit the Kenny-Imai model to a single t distro\n",
    "SeatExp = [statsT.cdf(math.log(districtLean[d]/(1.-districtLean[d])), nu,0,nuSigma) for d in range(nDrawnDistricts) ]\n",
    "\n",
    "SeatExp2 = [statsT.cdf(math.log(districtLean2[d]/(1.-districtLean2[d])), nu,0,nuSigma) for d in range(nDrawnDistricts2) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a679bb74-8c64-490c-ad25-2f6c2e7b7192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build county geom before we aggregate stats by county\n",
      "There appear to be 88 counties in OH .  I will assign them county Nos 0 - 87\n",
      "We will now build the county-by-county geometries\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Building county geom for county 80\n",
      "Here is your original county-based map b4 any triage\n"
     ]
    },
    {
     "data": {
      "image/png": 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UFqoTJCor/9///seyZcvYt28fISEh9OzZk2nTptGuXTsURUGWZa688krWrFnjcgz33nsvs2fP9vq3iMs6y9O/HaFHdARD4qJq3qEa8vPzGT1ad298/vnnneWKopCQ4CpOLFmyhFGjRrktrKj2aaLI/DynsPLqP+7kVNJATgFfZ2bTNNjM6qw8DhaV8HJKM1qEBKEJwbQDaXySnsX7J05zVUwEj1/UmC4RoV4da33z3sB3WLDq3zQIimFXUBPW/R5Ni+XfAvDSjZ0Z1aNZPY/Q4HyjMLeULd8e4o91JwmJtHDlrW1p2ysBuZbTnNu2bWPTpk0ATjsQB4cOHaJFixYV9lEVCeXyOB79e8cK28rz0IeLSQ1y795RW+bOnevX9gMJQ2Bxg2PFpfyYlceQhpEkBln0t3u7gBAky5glqdwbftnbvqPc7CJslK2bzlk3S2CSy+rKtXjDHrPyZ7bKCn//+999+hts27aNhx9+mB49emCz2XjiiSe49tpr2bVrFxaLxVnv7rvv5tlnn3Wuh4b65oEbZp9PLrTZvG7rgQceYPjw4QwcONBFYDmXrVu3kpqayttvv+1224ok8XJKUyYCj7w9n8uSmvD18VPO7VP2n3Cp32vjn7QODeKsVeW01cYDSY3oHhnK8wfSGLxlL4NiI3mqZSJtwqpXUQcay97ZQUiEhT79YuhxaBcAKcXvU0LZuRIXcYEkhTOoE0qLbaSuPMr2VcdQFImeIy+i05VNMVs893hbvXq1c7l///4ID7MQC0m3T6kJkwBNNozbfYUhsLiBQ3B4ICmeHlFh9Tya6jFJoPlBA7l8+XKX9ffff59GjRqxdetW+vXr5ywPDQ2toKXwBaEWXWApUlWv2vnkk0/Ytm0bmzdvrrHu3LlzadeuHX369PGoj1sTG/LPxrHOKZ2nWzchvcRKnk0ls9RKuEkhRJb58Uwup602ilSNSJPCwNhIutvPr8ENo/gqM5sZB9Po/+turoqNZPJFjekQHuL5QdcDB7frQtofa2FV4uvsLQx3Cisz+j5DXOgZejX/qT6HaHAekX4wh+VzdlKcb6XzlU3pNqQ5wWHeG8XWFk2WcGcWXRYC9QKY2g0UDIHFDRzyseahFF4fKJJEXXj+5+TkABATE+NS/tFHH7FgwQISEhIYMWIEU6ZM8YmWxaFhKfYicuSxY8d4+OGHWblyZY1GdUVFRSxcuJApU6bUqq9z7U8SgswkBJlpXU5TUp3WRJEkbohvwPC4KD5LP8tbRzMYsHkPA2Iieah5I3pF+1fN7C3/eqs/X7/5Gxdf3Yy7gpPYtHorK79ezg2HfiZkg5ncKa0JDjamgwxqZtf6k6z5ZA/xzSO58bHuRMTUv7ZRSCDUmp8HCqBJhobFVxi/pBs4bLi8MUtVVZUpU6aQnJxMSEgILVu25LnnnnNRRd5+++1IkuTyGTJkiEf9mCTJ7xK9pmk88sgjXHbZZS7GbP/4xz9YsGABP/30E5MnT+bDDz/kn//8p0/6lGUZRVUp8kJg2bp1K5mZmXTr1g2TyYTJZGLNmjW88cYbmEwm1HLam8WLF1NYWMhtt93mi+HXmiBZ5p+Jsay7tB1vtUvieEkp123fz3Xb9rHydE5ACtG5pzNRbSWMuOMi5FlTOXjNCOJemco/9qwiuLQIkZHLJX2/vCCMig38h6pqrF+8j58W7KZdn0RGjr84IIQVsAssbl97gXeNnq8YGhY3kO0aC28eDi+++CKzZs1i/vz5dOjQgS1btjB27FiioqIYN26cs96QIUOYN2+ecz0oyLN5fkXC7xqWBx54gJ07d7J+/XqX8vKRITt16kTjxo0ZMGAABw4coGXLll73axIqxW681VSFO+6NDubOncu1115LXFxcrfvzJWZZ4m8JMdwQ34DvT+fy+pEMbt1xiNahQdzTLI6/xccQEgCB4HIy0/m/h+5CMZkYvHUPAGF9+6IVFVG0dSsRgwaR8PQU5Bo0XAZ/bfKyivn+/3aSeTiPvqNa0+Wq2mvjVE3jzS93U5Rn1d2NHbcQTZTJEqLcsgbg8FE+Z5t9PaREoLlhw2LgWwyBxQ0cGhZvTs9ffvmFkSNHMnz4cABatGjBxx9/zK+//upSLygoyCsbEIskYfOjkdeDDz7IN998w9q1a2natGm1dXv27AnA/v37fSOw2IPC1Zaa3Bsd7N+/n7Vr1/Ldd9/Vui9/IUsSQ+KiGNwwks05Bbxz/BSP7TnOUxu2Y573Nuk/r8VWXESrVq2Y99579OjRA4AvvviC2bNns3XrVrKysti+fTtdu3b1+fgi4+JJ6tiF7sOvI77YytE77qRg/XqEohD70EM0uv8+jzUrjjdZQyNzYSOE4MCBA6xduZGinfGER4Vy/aPdSLjIO6/AQ5kFmL9PxwwUhukvJUICJPs9XcLpmuwoB6lsuVw9JD33ckGsmS7tY2vu3DhlfYohsLiBw+jWG4G6T58+zJkzh71799KmTRt+++031q9fz6uvvupSb/Xq1TRq1IgGDRpw1VVX8fzzzxMb68aFYSdChhKTmWe27KRnQkOGNPWNAawQgoceeoglS5awevVqkpOTa9wnNTUVgMaNG3vdf3ZRMQWWIIq1Qq/bqon33nuPpk2bMmjQIJfyzek5fLU7gxKbilXVUDXdjVkVAk2ATdVQAU1zlAk0Rx1NoFmtRBdm0jzSitBUhKqipJ9AKSyguGlz3etACITQ9Ie0ptnXhf0tT7O/FZZ9YoTGyPx8Fr7xOkkXXcTIW/5BaGgIBRnpfLb4cw4dOkRcXBxbtmzhoosu4rLLLvNJyPGqkCSJm6ZMA6B4924Actu1Z/I1N/P+P0bWKHR89OdHzPptFpfEX8IDXR9AkRVGfjmSkS1H8nzfqj26DM5f0tLS2LNnD3/++ScZGRlEWJsRpMrc/OSlPjGsLbXqU70X3ZnC0B5NvG7PoP4wBBY3cOgrVC+mhCZNmkRubi5t27ZFURRUVWXatGmMHj3aWWfIkCHccMMNJCcnc+DAAZ544gmGDh3Khg0bKg1eVBmtwkIgR2NWno1Zeemk+0hgeeCBB1i4cCFfffUVERERpKfrMUaioqIICQnhwIEDLFy4kGHDhhEbG8vvv//O+PHj6devnzN/hzfM/0OfXoiz+PaULe/e6OCFF17ghRdecCmbsekQs5f+CfYpKeF47trfuqRyyzi+ym2TJAlKbZhUQeOGR3VXR1khvLAAAFtxCciyXl+WQZaQzBKSJOv7yvq3LOnbZElGkiVkWWbDxwtokJDA35+YgipJZJTaaGFSuDwimPSTJzl8+DDh4eFYLBbOnj0L6BoXq9VKly5dXNzSfUlw27a02/0nQgjuzzhLUkj105tb0rcw49cZAPxw9Ad+OPqDc1ur6FZV7WZwnnL27FmWLl3KoUOHCA4OpkWLFgwZMoQ/f07j4PoigkJ9c62X2nStrMlUD1OmknRBmbB46v7tawyBxQ18YXS7aNEiPvroIxYuXEiHDh1ITU3lkUceITExkTFjxgC4xE7p1KkTnTt3pmXLlqxevZoBAwa41c8/u3dhcH4BV234A5sPr5RZs2YBesyC8sybN4/bb78di8XCqlWrmDlzJgUFBTRr1owbb7yRp556yif9l9g0QOLpXt1qrOtrnlqzlw+X7SMmIYwld/SkUYgFiyKjVBJBtzr+s3AtC38/y0uPTvDp+BY9M4Ubhgzmt08WsGbNGpo0acL999/P0L/d7VJPVVX27t3L66+/TnBwMN999x1r1qxhyJAhFabKfIkk6fY31WHTbIxdMRaAUW1G0adJH9afWM+PR38kqziLXom9/DY+g7pFCMHvv//Od999R3BwMDfffDNt2rRxvpTt3ZiJhITNZvNJPh+rQ2BRjPkZbwiEKVlDYHEDxQdGtxMnTmTSpElOoaRTp04cOXKE6dOnOwWWc7noooto2LAh+/fvd1tgAYgLD6OZWkKu99H2ndQkWTdr1qxClFtf4uhdqeOsx69tOcyHK/fTqGkEq++5jLBaBKlyoMgSAgkhhE8v/oMHDzJr1iwmTJjAE088webNmxk3bhwWi8Xl3FIUhZAQPY7LsGHDaN68OStXrmTx4sXIsuzTVPRCCI4cOcL+/fuxWCxcfvnl1R6zSTbx7+7/5r9b/8uivYtYtHcRAO1i2pFVnEVOSY7PxmZQf+Tn5/PNN9+we/duOnXqxPDhwyuEGFDsxuM2m+ojgUW/e5jP0bBMnz6dL774gt27dxMSEkKfPn148cUXSUlJAeDw4cNVTn0vWrSIm266yeuxGXiGIbC4geM+642+orCwEPkcY1hFUarN4XP8+HHOnDlTKxsQGe80QoGGnvRAqlMbtv+s38/73+whvGEwC/7R3SthBfTzSPODwKJpGpdccolzGuviiy9m586dzJ49u0phGKBBgwbcdNNNfPTRRyxevJjbb7+dpKQkn4xn8eLF7Nq1y1lWWFjI4MGDqz3u2zvezqiUUew5u4fblunu5GeKz/BC3xfo2bin1+MyqF/++OMPvv1WT8swatSoKgVkR4h9m9UKId57k1ntoRDOFVjWrFnDAw884BK9e9CgQezatYuwsDCaNWtGWhW5xYYOHepm7/WvlbiQMAQWNyhza659GyNGjGDatGkkJSXRoUMHtm/fzquvvsodd9wB6G8ezzzzDDfeeCMJCQkcOHCAxx57jFatWjF4sOcZdi+0y8Tx29cmXYEn5BZbefynPSzfcAxRqhGbGMavD/TD5AOXYX+NvHHjxhVu/u3atePzzz+veUySxC233MKrr77KsmXL+Mc//kFERIRX4ykuLmbXrl0oisKTTz7Jr7/+yvLlyyksLGTIkCHVBhIMNYdycaOL2TFmB1bNilmuv2imBr5j+fLlbNy4kTZt2nDttddWm5vLZLdTmz/pl3MuGtcrSEhQIpv0N4FzLy7hsDOTkDVBMBB0jsBSU/RuX+QWC4QMxxcShsDiBmU2LLU/8d58802mTJnC/fffT2ZmJomJidx77708/fTTgK5t+f3335k/fz7Z2dkkJiYyaNAgnnvuOY9jsYB+nVxYGhadc7VUvkDTNF7/9QhzNxwmP0P3QmrcIooeyTE8ftlFPhFWoGwO2NeGa5dddhl79uxxKdu7dy/Nmzd3a39F0XNPffrpp8yaNYvhw4fTvn37WmuBQkNDCQ8PJz8/H4BevXphNpv5/vvvOX78OLfddhvR0dE1tmMIK+c/2dnZzJw5E4D4+Hj+/ve/13gNX3x5a86k5WCzSfZwKKLct7CHQhFkWFU2BIeQbDGRGBpM+dNVKneJSYAp1ESn5tHV9ltV9G4HtcktduG9OtYvhsDiBmVeQrVvIyIigpkzZzov3nMJCQlhxYoVte/gHOQLyzjdL9bp/9t2lHk/H+LMmSK0YpXgBkEMu6I5beMieLBbM78IR/5g/Pjx9OnThxdeeIFRo0bx66+/MmfOHObMmeOsk5WVxdGjRzl58iSAU8BJSEggISGBZs2a8a9//Yuvv/6azz77jMTERPr160ebNm08/h2KiorIz8+nZcuWzn27d+9OcnIyH3zwAQsWLGDIkCEEBwdz+vRpTp8+ze+//05CQgLx8fHExcXRunVrp72NwflDUVER+/bt4+DBg86wBkFBQfTq1Yv+/fu7JQRHRIdy/T39aqy3Yv8hZhzLYVxyLFe08C7NQ1XRu8tT29xiBr7DK4FlxowZTJ48mYcfftj5IJ4zZw4LFy5k27Zt5OXlcfbs2RrfpqZOncozzzzjUpaSksJuexyH+kaxX2TeuDXXNRIgLiDpXgD46Pc/U1TKsPc3kXEkF0uDINq1iWVYu3ju79rMr5bwTlsoH59HPXr0YMmSJUyePJlnn32W5ORkZs6c6eIyv3TpUsaOHetcdxh//+c//3HGZQkPD+eWW27h0KFD/Pjjj3zyySfExsYyYMAAUlJS3HKtz8jI4Msv9bD758axiYmJYfTo0Xz22WcsWLDAWV7eNT4tLY28vDxiYmK47777fGJ0aeBfNE1j3759bN68mYMHD6JpGvHx8QQHB3PppZdy2WWX1UpLXGO/9uvIF9PEVUXvdlDr3GIXzi04IKi1wLJ582beeeedCjE2HPPUQ4YMYfLkyW6316FDB1atWlU2MFPgKH/Ox3NOdkRqvEDQTW69f9DnlVq54n/ryc8qZvSQ1jzXr1WdaVIke5RMf2iLrrnmGq655poqt99+++3cfvvtbrWVnJzMnXfeyc6dO9m0aROLFi0iIiKCLl260KxZMxITE13sXDRNIyMjg99//51NmzbRoEEDxo4dS3x8fIW24+LiuO+++8jIyECSJCIiIirYtKxZs4affvqJ3Nxcj4ImGtQ9e/bsYcWKFWRlZdGkSROGDBlC27ZtiYyM9HvfjqvI29ucO9G7AyW32F+dWkkF+fn5jB49mnfffZfnn3eNPvnII48AlQfkqnYgJpNXIen9idP2oJ7H4QkScEqxcM3ydc5xXxoZyn/6dK/PYdUageu8dG2Z9/tJ8k8V8dhNHbm/u3s2Hr5C9pMNi7/o2LEjHTt2JD09nc2bN7Nt2zaXN9CoqChkWSYnJwdN0zCbzfTv358+ffpU+8IhSZLzWhdC8Mcff/DZZ5/RrVs3mjdvzk8//URsbKxbdi5/FfRzRlBYeJCssxvIzf2N4uKTKEowQmgkNv4bFsslFBaqJCYm+lVTuHbtWl544QU2bdpEdnY2Dz/8MI8++qjzYV9V3y+99BITJ0702Tgcl1FtNSyeRO+ufW6xC2xuvp6plcDywAMPMHz4cAYOHFhBYKkt+/btIzExkeDgYHr37s306dOrdLEsKSmhpKTEuZ6bm+uTMVSF4/07EDPjVsXgRtGcSD/LGXTh5WBwBAdzi/hPfQ+slug3bO9vwjvScxHAHV2qz4PkNwSs/GQBiqK4aFuEPSlbw8Qm9Bo4qPo26piEhARGjBjBNddcQ3Z2NseOHWP37t2YzWbCwsKIjo4mLi6OZs2aOQWV4uJiDhw4QE5ODm3atMFqtfLDDz+wf/9+QJ9+io6Oxmq1kpGRAcC2bdvYtm0boAtL7kZ3vlBR1SJyclM5lbmC4yc+dJZLkpmI8HaEhCSRk7ON4pKTZGWtQwgze3b3pLS0K+3bt6dly5bExsY6BcvqsNlsZGdnk5+fT2lpqTNoW0xMDNHR0c7o3GlpaaxatYqioiKuvfZaPvjgA6644goXzcS5rsDLli3jzjvv5MYbb/Tp7+O4H9f2rlBT9G4H3uQWi7VmMDZ7GQWvvFsW/lpyfGRAj1it5y6SndskxzKy3evDXl8qty4r9jL7t6zoy1Au1HZ5JFwskx3bKxX4HJkfsUuGguCg40iiyOPfwJd4LLB88sknbNu2jc2bN/tsED179uT9998nJSWFtLQ0nnnmGS6//HJ27txZqYvl9OnTK9i8+BNfRLqta+6+uCPl45yOXfUzm2q4tIUjbw1U+C6/7Igd47LN7ncsKLefcN2vyrZFZdvKfm0hBCXFpYB38+B5pVY2HTiDHKQQbKr7h6Eo0S/23d9+UWWd3ZJEz6sG6uH5AwxN0yguLiY+Pp5GjRqhaXrOI03T0DSNEydOkJ+fz7Fjx0hNTaW4uBiA77//HoCGDRsCust1fHw8hw8fJiwsjEGDBpGamuqSSTs1NZUru7WGjbOgx50Qc1HdH3AdU1ycRmbmMrJztlBcdJz8gt0IoTq3h4e3p1XLx4iK6obJFOayb1HRMdatH0Fik7OYTa3ZsWMHGzZsAHQvsAYNGhATE0NsbCwxMTGoqsqZM2fIysrizJkz5OTkVKn5k2WZmJgY8vLyKCkpITIykhdeeIHu3bvzwQcfVNConKsp/+qrr7jyyiu56CLf/g+dU0K1lFhqit7toKrcYu7Qu2Qjl536ElGqX89lQ7WPvpzbs6ssUU4YK+/9VI+3hdCOUGj5vf4GgIcCy7Fjx3j44YdZuXJlheiE3lA+CE/nzp3p2bMnzZs3Z9GiRdx5550V6k+ePJkJE8rCm+fm5tKsmXdW4tXhizgs9U1IoUKYbObIpDVIlQRgq9uQbJ7TMMmM0qZ2V+vZYivD39/EiSM5SALad/RUresbwkJCgEJumfYqDRon2nMMyYAuKP7w2SfsXvYlJcVFBIeG1dBa3ZKXl8f777/PmTNnaqwbFRVF586d6d27N6+//rqz/O8330zDKlTqDgP7YcOG8d133+m2K9+Mh33fw4a34d+7ISIwp4w9QQjBmTOriYnpgxCQk7OVM2dWcyZrLQUF++y1JOLjryGxyd+JjupOWFhr+3lSNSEhzVBtYQQFRTBs6DUMGzaM7OxsF6EkKyuL3bt3k52d7RRCYmNjad++vXM5IiKCoKAgFEWhtLSUrKwspydXWFgYycnJNGnSxG3tV0ZGBt9++y3z58/38periLdGt+5OzVaWW8xdTNFRHM9NovlU7x70jmSoLt+qDTQVVCtC00Cz6QlShebqoOB8MXS8BIpy28tpUYSe/sRFE1TuW/5oKCHtUrw6Dm/xSGDZunUrmZmZdOtWls9FVVXWrl3LW2+9RUlJiU/UuNHR0bRp08apPj6XoKAgv1idV4Uv4rDUN1GWcIRaSl5XizMfl+PNqLI5Z2eZdO56WV2H9C+do1p0bU5yqePcVr6dc/tCqlCu5ZZSG3FF0zSu/2QLJ4/mMKBPM4a1acTfUurnwSc0/W05PDqakLCKgaeCwnVtYnFBQUAJLEIIvv76a4qLi7ntttswm816IkZZdn4c/6uQkBCXoFp94/JYfyqCR3mH8Ldfg/bXwZAZEOkavXnYsGEMGTIERVFo0aKF3sahCF1gQcBbPeBf66FB3dod+ZoffypL4ihJCkKoWCyNiI29ghYtHiA25grM5loarEqlyJJ+X3QIJJXFFFFV1fn/q47Q0FCio6O90ozMnz+fiIgIbrjhhlq3URW+Mrr1JwLJJ84CkiRBuWerfsx19wwEIKRB7dVZPsIjgWXAgAEualuAsWPH0rZtWx5//HGfzTnn5+dz4MABbr31Vp+05y2Of9F5ZMJSAZOiIAmJjn/vXd9DqRXBa/dBab5bdef9fpwZX+9CE2DTBKLQxvX9W/DakA5+HmX1aKqKQEIxVe6qa7FrLdd+uZjg8AjnFJ0+vVYWNMsxp6yfj/p2h2ZZVW3IsoIpKKjsNlnJieuccnPZVNZm+V3z8/I4dOAAF198MfmH9nJRt0sJb1B9MkMHA5sWM/DU/5UV7PpS/3S/HYb9FxT9FiRJEsqm/4FqpZEkwaqpIClMPzOEL75YzO7TuYRMu4g+vXvz4ltznfleoHahFOoSIQRZWes4euw9l/LWrZ4gOron4eFtfWIkK4SGSak583Zd2ga99957jB492qcaeQfO/GL1OU/iBtL5/OAIMDwSWCIiIioE1QkLCyM2NtZZnp6eTnp6ulM7smPHDiIiIkhKSnJK+wMGDOD666/nwQcfBODRRx9lxIgRNG/enJMnT/Kf//wHRVG45ZZbvD5AXyBJ0nkfOfZ8z3IuBMhuHMDBnEJmrNhDcb6VNh0aEiokBrWP54FKPIJUVWXq1KksWLCA9PR0EhMTuf3223nqqadcHiB//vknjz/+OGvWrMFms9G+fXs+//xzj/PuCHtOE6WK2CINmzRDILFvlWfGfdO++ZGzhRWN4fq0bM4N3Tuy8cBRth09wYmzuZTYbDx33SBCLO7HNxHo73K7lx/lTyHodePfuWzUP2vYScCBH+Dwz2VlU85A5i5da7J6Opw5AL0fhKimsPYl2PVVWd3Y1nBmH2uWfc4DPSz06NgaW1ADnvjwFwb168mufYcJi4wGah9Kwd8IIcjM/I6Dh16jsPAQ4eHt6NBhJvGNhiFJvhcaJElDkgPHUHndunXs2bOHTz/91C/tO6aEUrOyaR7bgNjgOtY4uEMAZDi+kPB5sJPZs2e7GMT266dHLCxvyHTgwAFOnz7trHP8+HFuueUWzpw5Q1xcHH379mXjxo21cCHzHxLnl5fQueiB5M5vqrr0NSFYtDudd345xKGD2SAEIy5vzlvDKo9Y6eDFF19k1qxZzJ8/nw4dOrBlyxbGjh1LVFQU48aNA/RztW/fvtx5550888wzREZG8scff9TqjVHTVJCkKgWW9hd3o8Xchag2G5IsI9s9BWRZRpJluwOBXDbFZr8Z/uPUKVRVdfaxc8cOhl8zgv+8PZt+/a5Ae+stutgNYKc8/TT3vPMB0dHRZVNv5aYGqpsmlGWZd/51W+X2FPmnYPkkOLwOwuKgOBdyjkLDNtDvMeg/SfdiaNzZ/ukKPzwDH99sb7zcb/LQNohtCQVnWD7iG1AskHw5RDTm/c7P0ui6Z9k69XL6TXgfmnavdSgFf5GXv5vjxz8kJ2er0y6lY4c3aNRomF/djfUJiMARWObOnUv37t3p0qWLX9oPN+uPrydOF/PGia2kDgm8CLTigrjzBg5eCyzn3iSmTp3qjJxZFYcPH3ZZ/+STT7wdht/RgLePZrItt8DujqqX25XzToNchzOYQ7gp5xyma/URzv1Fhe2ibLncd1lfooryc7YJ10tEAGfUElQhWDRvGw1MJn27VLYdcAaac6zHto3l0u5Nqv1dZs2axaxZs5z/0w4dOvD0009XyGYqhGDYsGEsX76cJUuWcN1111Xb7rmc69Scnl/Cm5sPs/bAaU6czEcrtCGFKFx6cQLTBrSldYOqE+w5+OWXXxg5ciTDhw8HoEWLFnz88cf8+uuvzjpPPvkkw4YN46WXXnKWtWzZ0qOxO7BabchCxVTFlBBAaLjniQfPzeb96quv0bJlS64epGdHdsS+cFyrFksQFkvt3kZtNht/rFnF0Z2/AXbBJvcknD1o//80steMQpI60mfgozTpe33FhloPhFYD4NQeKDgFiRdD0Dl2PWGx0H2MS1FOh1uBZ4kJt8D/XQWX3AFXP1dxX1+SmwZb3ydXZHDA/CdqSASaKEWWLEiyBVkyI8lmVLWQ4uKTFBcfA6BRo+G0af00DRr09rOg4kCrEzeS/Px8F/vCQ4cOkZqaSkxMjFPrmJuby2effcZ///tfv41j0EXNeTJ3F9NybKQH1Xy91xeGjsV3BE442fOAI8WlHEkv9Wsfpb9tpfDTD7Du24V25jTRz75KcN8rndvVrDPkvfs6pVs2oOXnE9S5G5EPPYapqeuUx7kXiVkR9MhWiTtRisXxFl2unsMGQrKLBtHFKmlphVCDwNK0aVNmzJhB69atEUIwf/58Ro4cyfbt2+nQocxmZObMmV7dtEtVlaIzxTy4/A8SooJ57/t9aEUqligLrVtEc03beO7tloTF5P4Nu0+fPsyZM4e9e/fSpk0bfvvtN9avX8+rr74K6Aa73377LY899hiDBw9m+/btJCcnM3nyZI8FLoBSm4YiNL+6LJeWlrJgwQImTJjgl4dkz5F/49TRw05pWQBYbJC3GxEaC8FRzroHjuRw6OfvKxdYQNcQNWoLtHWrb5d8L/9ZDd8/CZtmw5b3IK4tNH/Um0NzpbQAdnymT1Md+AkQZDUNIeuiMBJiRqIooWhaCZqwITQrmrASFNSIyIiORER0JDb2CszmqBq78SWSJJBqZZruGVu2bOHKK8vuSQ6PzTFjxvD+++8D+kuoEMKv0/pf7zvEtByb39r3CRfalFA9zzIYAoubmICnWyVye5OGyOgPeFmSfP5QWFacxs8DLqf7Y49www03MK9TMtdd2RXQtRR9+vTBbDbz32XfERkZyauvvsryp8axa9cuwsJ851ny3f9tI/xszcLZiBEjXNanTZvGrFmz2Lhxo1NgSU1N5b///S9btmypoA1wl8PH8rD8mcU3ZOkFisTLt3fnpra19/iZNGkSubm5tG3b1hkYa9q0ac4cPJmZmeTn5zNjxgyef/55XnzxRZYvX84NN9zATz/9xBVXXOFRf1abiuxnS6gvv/yS7Oxst8Pwe8olI9z39nh/7BBKiwp81rdLvhfFBENfhF73way+cGo3bL5dr5i6kDMHLJyZO4/Wq39CstRsiOrk1F7YMhdSP4aSXEjuB8Nehg43IG+egGxdR4cOr/rsmHyLhiT5/5bev3//Gl2C77nnHu655x6/jiO7WA8e+nqjEPo1S/RrX7VF4Duj2xYtWnDkyJEK5ffffz8TJ06sMlLvokWLuOmmm3wyhvrGEFjcRJElFEnC4ueAXkOHDq0wneJg3759bNy4kZ07dzqFgVmzZpGQkMDHH3/MXXfd5bNxCAlkD5+tqqry2WefUVBQQO/eujdSYWEh//jHP3j77be9Sr2QIhRWAtueHcTx3BJiQsw0CvPOyG7RokV89NFHLFy4kA4dOpCamsojjzxCYmIiY8aMcQbIGzlyJOPHjwega9eu/PLLL8yePdtjgaXEpqL4WWCZO3cuQ4cOJTGx/m/gFrNMaXFJzRXdoMp8Lw1awBPH4cgGWPoRzH8blk8m80v9+Iu/f5+QYXfq9jNVkZeuGwLv/AIO/gShDfVgdd1vd3GjVkxhaKr+4lA3UzyeIUkioIxu/Y3D061HkwQaR3o+lVonOLXW3rN582anrRrAzp07ufrqq7npppto1qxZhQjDc+bM4eWXX67yeeIxAXDOGwKLmwSC6ZQjHUF5g09ZlgkKCmL9+vW+FVhkCdnNN4MdO3bQu3dviouLCQ8PZ8mSJbRv3x6A8ePH06dPH0aOHOnlgCAIiXCLmbYNfZPBd+LEiUyaNMmZubhTp04cOXKE6dOnM2bMGBo2bIjJZHIei4N27dpVmdW1OqyqhsmPAsuRI0dYtWoVX3xRdSTdusRsVrCWWr1qw+18L817Q6cS4G24axVNY99CPf4nIVsmwr43IWUoNLtUN+Y1BetCytENcOBHOL5Zt/1I6g3XzYaON4CpojAsm0KhVELYipDMtbeZqCzLPcCGDRt48skn2bRpE4qi0LVrV1asWOESJr56NOQ60LAECprmiBBb/w/SqhA+HNu5TigzZsygZcuWXHHFFS75uRwsWbKEUaNGucRFOt/565zdXhIIl0Tbtm1JSkpi8uTJvPPOO4SFhfHaa69x/PjxCtK1twhJcsuNGCAlJYXU1FRycnJYvHgxY8aMYc2aNezfv58ff/yR7du3ez2eb0+eJc/HImNhYWGF4FmKojg1KxaLhR49erBnzx6XOnv37qV5c88DmFltGoofxd558+bRqFEjpxFxfWOxmCj1UmBxJ99LhVAKacVEXD2epGbNIHMD7F0O+1bAr++4Nh4SA8376EJKq4EQXr1XomwPh6+WZCHXUmCpKsv9hg0bnG7Zb775JiaTid9++82jTOKSJJDlv84t3TEtJQdwHBZfBY47l5ps1bZu3Upqaipvv/22z/uuT/46Z7fX1L/IYjab+eKLL7jzzjuJiYlBURQGDhzI0KFDfZ4B2BONksVioVUrPYJn9+7d2bx5M6+//johISEcOHCgQhCvG2+8kcsvv9wjN9RwP+T+GTFiBNOmTSMpKYkOHTqwfft2Xn31Ve644w5nnYkTJ3LzzTfTr18/rrzySpYvX87XX39dKxdaq6qh+CLldCVomsa8efMYM2ZMhUzJ7sRG8gcWi5nCQu+mhNzJ91JjKIV21+gbCrPg7GE9hHlwNMS2Ag8EAsUegVYrzoJwz5NnVpflfvz48YwbN45JkyY5y8oHx6sJIQSyrCLLHtjrnOcI+4tFIObd8jc12arNnTuXdu3a0adP4Ll6e4MhsHhAIIRh6d69u1ObUVpaSlxcHD179uSSSy7xaT/eiGeaplFSUsIzzzxTYZqqU6dOvPbaaxWMdWvi8rhI9p0t9GJUFXnzzTeZMmUK999/P5mZmSQmJnLvvffy9NNPO+tcf/31zJ49m+nTpzNu3DhSUlL4/PPP6du3r8f9WVUNxU+C76pVqzh69KiLsOXAndhI/sASZMFq9W4KzB1B3J1QCgCExuifWiJbdIFFLTlbq/2rynKfmZnJpk2bGD16NH369OHAgQO0bduWadOmuX2eqaoVSRLkF6xny5bZ+lu3pLsH6IEv9Zg+EjKhoQ1o3Xp4jfmJfMnKbfv4ZW86zWLCdHtAWUKRZeeySZGRZQmTLDvLTYqMosiYFRmLScasKCiyjMm+nJNfAiUm9uQWU2ATBNltDIMUmSBZ95eSKk0L4viSXMwyHAFCy+pWfq1WdkYKIdDKh6WwL5dqooo9vKM6W7WioiIWLlzIlClTfN5vfWMILG6iR4oNAInFTlSU7jK5b98+tmzZwnPPPefT9t3VsEyePJmhQ4eSlJREXl4eCxcuZPXq1axYsYKEhIRKDW2TkpKqtkWoAiEJnz/qIyIimDlzposdQWXccccdlQoCnmJVBR54XXvEoEGDqny4u/1A9zHm4GBKA9zr1BNMlmgA1FLPBZbqstwfPHgQ0P9Pr7zyCl27duWDDz5gwIAB7Ny5k9atW9fYviRJWK0hmM27ycndXW3dM1kQ3SCeRnGXenwcteXuRXvtS7k+bTcIuHt1uk/brAyXK6vczbGme9IE0yk6mNUaanlGTbZqixcvprCwkNtuu82n/eoYbs3nBXVldFtTUKbPPvuMuLg4kpKS2LFjBw8//DDXXXddrVKf14Q7x5uZmcltt91GWlqaM0vvihUruPrqq30+nvMdqyowBbCBoK+xBAdT6tt7db2iWBoAnmtYaspy77CZuvfeexk7diwAF198MT/88APvvfce06dPr3lsipkBV22htLQQITSEUAGBpmn2dQ0QHDjwMzm5U7DZ/BtP6lxu7RDMh38Uc2VjladH9cGmaqiqhk3Tv1VN6GVCs28TqJq+bNMEpTbVWcdm3++nA3msOVzAzf1aEBsdjE0TWAVYNQ1rueiZjvRY5V84RbkFgWukTVe5X7isO3JtOSJNlyWA1dGjUZfFuYrdFoxS6NsnR022anPnzuXaa68NqEjxvsIQWNxEom6mhGoKypSWlsaECRPIyMigcePG3HbbbX5R/bmrCJg7d65H7dbW1kaWpPM6lxOAVROY/jryCpaQMKzqhWNfoATr00lqaY5H+9WU5d5h1F2ZN9rRo0fd7sdsDsZsrj5lRHDwbnJygToO4X/z5R348I+thAWbSW7c0Cdt5sr7WHN4L6M7NaFzs2iftOlr1u0PAR/OZFdnqwawf/9+1q5dy3ffeZaP7HzBEFjcpK40LDUFZRo3bpwzz42/CZwJMD0OjhpQI/IcqyYwXzjP7xqxhIZhEzJaaQlyLVMBBBJKcCwAqtWzaY2astxfdNFFJCYmVuqN5rMYGnYkSbV/163A0qF5PI0tJaw6YvJZHBvnfTKQXwIk2adeQtXZqoGeHbtp06Z+0bgHAn+h26d3nO/Zjj0lEOLOlMckSZzvswtWjb+UwGIO1eM/lOaerqHm+YESZBdYbHke7efIcl/+Uz7LvSPf0xtvvMHixYvZv38/U6ZMYffu3dx5550+PgqHnrJuT0RJkuiVFE6xppA8+TuKSrxzd4ey+5McwAJL+fQnvsBhq9amTZtKt7/wwgscPXrUI3d496n/H9rQsLiJFAD/rLpEQjd0DRRMyvkvsNg0CPkLCSyqRY8++uKs/0MJDcMkS5gV3RPEpEi614fdC8SkKCiKgiTr37LJhCybkBUFWTGjKCYkRUGRJWRJRpYkZLuniWT3LJFl3atEkRUUk2L3NtH7UBTFWS5LCrKse6XIjvZkSW9Twl4u2/vRgzMiSUj2yKrCVuTz3+qRRx6huLiY8ePHk5WVRZcuXVi5cmWtE21WhXBcRaLuI+K+OOYqfn7mSzJtIVw6dSm/v3CDV5qWQPDarAnxF3vR9TeGwOIBf6UTL9DEM5Msn/cCi1XoD+y/CmnBFwHwbXYLwq0mbELCJmSs9m+bkLGhf1trvBWp9k/9IKOhoPG/qyV2Zit4m/igsjg+kyZNconD4ilnz56iqCgHTVMRQkXTVLtBr2NZJTPjMDk5cezde4STJzW7Ua5wfpdfrmxbZXX3ZIMaHA2ShKoJ/SMEmiZQBfZvvfyyi2JYsreIPBHMiVNnadqo9m7mZYHjAvma8u2U0F8dQ2BxE93o9q914vnekbj2mBT9sldtGoq/fIP9jE1IWJTzc+y1IbSBblz537914vLe3WqorV9fqibQBGiqiqZaUUuL0VQrwlaKaitFE0LfJgSqKhBCQ1VVNKGhqSqqpmKzaWiaik3Vvx3eJpr9WwgNzdGPPX6GJkDTQKPsQSvAWU8VjlgbMqct1Wcwrw9OnTrI229/4GbtIUCq/eMdmoAPSnoA2YCeN0dCINvvHo51CXskXsCEQoRUgubl/cUemT+gBZYAHlrtMLI1nydoGoUFpWTnFjvVmI7gQALArvor501X5kYnyspdAgsJ13rOdUcAIvsfIUBogGZfFhqSgMS4MIKDfZNX51zkALNikW26Ot5qVc9bgcUqJGylJez4dZPzTnZu4CrXO5we9MsxfaGX6NlfhaZhs9nQVBWbTcWmWrFZbVitVmw2Gzab/q3aVOdDW7M/wDX7MkCh1UpaVjbJCY2wmBQkWeb0kcMEh4XRIKExkn26RJLKxiKhT6VIsmwPzqUvS7KEjL2OLHPyeBqa2UJBvntTKJIkYXJooEwyYIbQ2ufs8QfLVj6BGoAPody8kwBc1rcBCfFJ+v9AkpFkBVmSkSQFWVaQJJmCAg2TKcFeR3L5rqqs/Kd8eUFxKR/8dyPjLo3iwWsudZ4n5378gebQsNTCiOXEiRM8/vjjLFu2jMLCQlq1asW8efNcAnD++eefPP7446xZswabzUb79u35/PPPSUpK8qgvX2VrNjAEFrd5bVMhnbJyyeew3/o4JxBjjWxqEswVD/Xwy1g0PTG6X9quDdop/aFnK7ZBiH+ENH9TIpvJKS3i8++W1X3nwi4uO+NI6GVC0W8Be0+kgabiDCqRW8ihvIPe99uqM2fzPTNSDWQ0oSCE9wajvsZmLQag5UW9uOiinnXWr1UUABBkMWOx1G1aAIccoHgoEJ09e5bLLruMK6+8kmXLlhEXF8e+ffto0KCBs86BAwfo27cvd955J8888wyRkZH88ccflcbRqZYAznN0PmIILG7SXijktAwlq110hUe5JJ2zTiWPenuQofIRn8sb8jrOa2cYaSgz5Ld3IEkSQpKQZLB9fQi5yI9hRANLXiG0aSScPI16HutYhWIirkEUN/XvVFbmEuDqnGUEQnNMRWhlGjhJz5+iKAqKYtK/TSYsFgtm+ycoKMi+HKQbmsqKR2+6LvYKmoaqqXYNjX2aRdXLHVMxmiZctDea0MjJL2TJF59Tagnz+rebNWsWs2bN4vDhwwB06NCBp59+2un2m56ezsSJE1m5ciV5eXmkpKTw5JNPcuONN3rdd3k0FDQReOF7rTZdYDGZ6lYjZbNr6pR6cNVxaKY97fvFF1+kWbNmzJs3z1l2buTtJ598kmHDhvHSSy85y2pjAG3XxXu8n0HlGAKLm1gkiabNIunQ1/Msvf5gzfoTJKQX+619TQSUvIISrHs1qOr5Gz5OFRJRkeF0uKTuQqLXlvJTQAC10WlZbSpLvvic4hLvo6o2bdqUGTNm0Lp1a4QQzJ8/n5EjR7J9+3Y6dOjAbbfdRnZ2NkuXLqVhw4YsXLiQUaNGsWXLFi6++GKv+3egCRlNCzyBxWbVk0yaLSF12q9V1Q2hTfWQgFCzG7F4+g6zdOlSBg8ezE033cSaNWto0qQJ999/P3fffbe9XY1vv/2Wxx57jMGDB7N9+3aSk5OZPHky1113ncfjDKT76PmOoa9yFyECyoKq5bEiwqyCgwXFHCkq4XhxKeklVk6VWsmy2si1qRSoKiWaHtraU4PhQMqbBDhtG6y281dg8bfRbYsWLSq1H3jggQcAPfR7y5YtCQkJIS4ujpEjR7J7d/V5Z7zBbFKwCZniEu8yNoOeWXvYsGG0bt2aNm3aMG3aNMLDw9m4cSMAv/zyCw899BCXXnopF110EU899RTR0dFs3brV677LI1DsYe8DC1W1CywmD6csvO7XPQ1LTecmwIYNG7jqqqsICwsjMjKSfv36UVRUtf2T4w7laciJgwcPMmvWLFq3bs2KFSu47777GDduHPPnzwf0dCP5+fnMmDGDIUOG8P3333P99ddzww03sGbNGo/68nXguHolAB5/hobFXXxwzlVn6GW1Wnnqqaf47rvvOHjwIFFRUQwcOJAZM2ZUmpFzf3IYrQ4VYHluM7rjYpnTpzjnxBKAWcDehCCuesS9t3stsOQzFPuDXlPP34tfFRJmPwosmzdvRlXLHqY7d+7k6quv5qabbgL0TN+jR48mKSmJrKwspk6dyqBBgzh06BCK4p+4HKqkUOIDDYtLm6rKZ599RkFBAb179wagT58+fPrppwwfPpzo6GgWLVpEcXEx/fv392nfug1L3WlYVNXKmTOHXcoqe/nIzj7N6tWrXbJyA6SkpDiF0jlz5rBw4UK2bdtGXl4eZ8+eJTo62qvxFVv18624BhmupnNzw4YNDBkyhMmTJ/Pmm29iMpn47bff3AuA5mG8KE3TuOSSS3jhhRcAPW/Tzp07mT17NmPGjHHmdho5ciTjx48HoGvXrvzyyy/Mnj2bK664wv3OAuge6hsML6HzAy9tOmoy9CosLGTbtm1MmTKFLl26cPbsWR5++GGuvfZatmzZUqG97n9vx+HfMtE0dFsCdIFFCD3mgRCUuW0iiN6QQaMs9x8c+k0xcK42xxvcea1hQSbYj6Fuz012NmPGDFq2bOm8wd5zzz3ObS1atOD555+nS5cuHD582OcByhyokoK11HsNC8COHTvo3bs3xcXFhIeHs2TJEmf+nUWLFnHzzTcTGxuLyWQiNDSUJUuW0KpVK5/0XUbdCiyffvoCe/e6+5AQtG/fnh9++MFZUj7fTGFhIUOGDHEKBr4gM0+flt5zqvrp6ZrOzfHjxzNu3DiXODQpKSnVtulwa/bUhqVx48aV5m36/PPPAWjYsCEmk6nSOuvXr/eoLynAvC3PdwyBxV28nBKqydArKiqKlStXuuzz1ltvcemll3L06NEKrnQRUSF06ue+Pc13OSUk7nI/aZsIMBsWx5SQep4KLFZVQyARVEcu2aWlpSxYsIAJEyZUamxbUFDAvHnzSE5OplmzZn4bh5AUrFbfeNWkpKSQmppKTk4OixcvZsyYMaxZs4b27dszZcoUsrOzWbVqFQ0bNuTLL79k1KhRrFu3jk6dOtXcuJuoQkGW6u4cLCgoJjJSpX//Xs6yqoyns7KOsXZtDgkJCZVuf+SRR4DKg9bVFlGcD0Ci2f0Mf+eem5mZmWzatInRo0fTp08fDhw4QNu2bZk2bRp9+/atsh2nW7OH9+XLLrus0rxNzZvr91OLxUKPHj2qreM20l8tRrp/MQQWNxF4N0VSk6FXZeTk5CBJktdqW9C1pkKCEk1DRkKRqr/QHbFlvEW4BKZxXRaVlWvlYtKUBapBtunLR49loGgFgB7QyxnfRggXuxsJPcy6JGGPFaIfjiP8OuWWJXSvG2eodllBkiWkcqHf9bgjin4O2EO1I8t2b65y61X8pkV21XmIuW5Con/55ZdkZ2dz++23u5T/73//47HHHqOgoICUlBRWrlzpX3dU2YTNRwKLxWJxaky6d+/O5s2bef3113nsscd466232LlzJx06dACgS5curFu3jrfffpvZs2f7pH/QjW5Nct3ZsKiqRmioTLduN9RY96uvfmPfvk9ITEwkODiY3r17M336dI/jhnhCUJCe1LJJw2i39zn33Dx4UHefnzp1Kq+88gpdu3blgw8+YMCAAezcuZPWrVtX2o5DYPHUrXn8+PH06dOHF154gVGjRvHrr78yZ84c5syZ46wzceJEbr75Zvr168eVV17J8uXL+frrr30q7Bl4jiGwuIuXwX8chl4TJkzgiSeeYPPmzYwbNw6LxcKYMWMq1C8uLubxxx/nlltuITIy0qu+AU6J4/y3ycucXOhQZ9st7Mstl18PNQmCYgS8V36rXZhwfuOy7lqHSus6t0vn7ntuDdd9bcWJwP2MWXbAjaP1H5LQPA8EJUlodr91y5lMP4yqInPnzmXo0KEV7J9Gjx7N1VdfTVpaGq+88gqjRo3i559/9jy+hLsoJlSbf+KWaJpGSUkJhYX62/259g6KojjtEXzWZx0b3aqqwGJxTyvXs2dP3n//fVJSUkhLS+OZZ57h8ssvZ+fOnURERPhlfLI9jk9kRLjb+5x7bjr+R/feey9jx44FdLuSH374gffee4/p06dXPwYPp4R69OjBkiVLmDx5Ms8++yzJycnMnDmT0aNHO+tcf/31zJ49m+nTpzNu3DhSUlL4/PPPq9X4VIYkSReO0W0AYAgs7uKliqUmQ6/yWK1WRo0ahRCCWbNmeTNqJ0GWTDLMmVze7BZkWX+jFvZLSQgQkmTXUujfliKVpHyNCLN+iui3zHKRWKUy63ynpsYRL8YRS6ZCXUnfXQLJ0eI56866Utn+EhJHz+zlI/EOE8Qwmsa3cWpJpLJu9TJ7Wm0Ne2h1+71CD78uOaMNa07NjB4kT9hzn2iaPQKK3bPKEbZdCM0evl22a3/sD0KniseeGE841D6ibLtdLbw24yTthP+9OI4cOcKqVav44osvKmyLiooiKiqK1q1b06tXLxo0aMCSJUu45ZZb/DIWWTGhqd4LLJMnT2bo0KEkJSWRl5fHwoULWb16NStWrKBt27a0atWKe++9l1deeYXY2Fi+/PJLVq5cyTfffOODoyiPqY6NbnE7srMjJg1A586d6dmzJ82bN2fRokV+yPqsY7V7CZnczJFV2bnZuHFjgEptRo4ePVplWw4NS23uytdccw3XXHNNtXXuuOMO7rjjjlq0XoYwbFh8iiGwuIuXNqg1GXo5cAgrR44c4ccff/SJdgVwzrvPvGw8Iaa6jdXgCzZt+YpPC5bRp20SHXpeVd/D8Rir1cqJadMIkvyfh2bevHk0atSI4cOHV1vPERyuxAdux1Uhm8yoxQVet5OZmcltt91GWloaUVFRdO7cmRUrVnD11VcD8N133zFp0iRGjBhBfn4+rVq1Yv78+QwbNszrvl1RfBKHJTf3DKdPH8NF1i/n7eLI0lBSohIdXTshNzo6mjZt2rB//36vx1sVNpuubTK76WVW2bnZokULEhMTK7UZKS+EnYtDeVYfQevcJZA8LS8EDIHFE7w4+Woy9IIyYWXfvn389NNPxMbG1r7Dc7Ch32TN8vkZ1t6htTlf31X8lU/lXDRNY968eYwZM8bFQ+TgwYN8+umnDBo0iLi4OI4fP86MGTMICQnxw0O9DJPJjOqDB/zcuXOr3d66desKwr9fkEwgvHfT/r//e4ncXHdeHCIIqWUqivz8fA4cOMCtt95aq/3doUzDUrMWqKpzU5IkJk6cyH/+8x+6dOlC165dmT9/Prt372bx4sVVtue4FwRy8sP83Dyiyafkaf1eXt30kOR8KxaY5bKpTKumVNhLlL8fivL3xvI9nNubVGUd6Zw2NVVF2NuVTSYkBOFyITlKE2qfX9t7DIGljqjJ0MtqtfK3v/2Nbdu28c0336CqKunp6QDExMR4bRhptec/UaS6Mfr0Nc4pI3F+egk58PfoV61axdGjRyuosoODg1m3bh0zZ87k7NmzxMfH069fP3755RcaNWrkt/GYzGZKtMALtFZbSkUMQfJp79spheTkUnr2urwsHYPdEF3YM6kKu+V7y5a9qmvKyaOPPsqIESNo3rw5J0+e5D//+Q+Kojin+9LT00lPT3dqXHbs2EFERARJSUnExNTuMWSzz7ma3Zi2qurcBN2Dqbi4mPHjx5OVlUWXLl1YuXJlte72TqPbANawbKEbm0uKubVlWZi78t9lMbNcj8FSepbo7K2ciemDqtgFW0c+MMdEk3AsO5wW7NtFue/KysvXdykT9k2C0sICzp48Tmyz5piDggCJA0ePUGJLobdvfyKPMAQWT/Di9b4mQ68TJ06wdOlSQA9SVJ6ffvrJ6wBYNqFiFqZav+nXlN00Pz+fSZMm8eWXX3LmzBmSk5MZN24c//rXv7wa97mcrxoWp0GonzO3Dho0qNLAYomJiXz33Xd+7bsyzGYLcgCGsq8tZlMDROkhr9vRNIno6Aa0Tal6ysNTjh8/zi233MKZM2eIi4ujb9++bNy40RkDZfbs2S6B5fr16wfo0zTnepO5i9Xmfmj+qs5NB5MmTXKJw1Ij9qYCV1yBQlM0i9QhPHHX9bXav7IJ5BkzZjB58mQefvhhZs6cCejJGh999FHWr19PSUkJQ4YM4c033yQ+Pr72gz+H1Y8/TGNz5S7zdYUhsLiLD66K6gy9WrRo4XH4fE9QsaGI2mlX3MluOmHCBH788UcWLFhAixYt+P7777n//vtJTEzk2muv9Xr8jp9fnKcaFoegeL4KXLXFYrEgc+FoWIIsEai2qsPFu4umyZhMvp2e/eSTT6rdPnXqVKZOnerTPh3JD82mutfcOuOwBHCCGV+b3G7evJl33nmHzp07O8sKCgoYNGgQXbp04ccffwRgypQpjBgxgo0bN7oXLfg84cI5krrgPH7alAobJmp3Uykf9O7SSy8lOTmZQYMGuahrf/nlF8aMGUP//v1p0aIF99xzD126dOHXX3/1yfjLHvi1+yfMmjWLzp07ExkZSWRkJL1792bZsmXO7XPmzKF///5ERkYiSRLZ2dm+GLYT5/j9rGEJNIKCLJjOUyGzMmQlnGDF+6SjmiajKOenPVl5HDYs/kw5URWqYzrKT2klfIEvzWvy8/MZPXo07777rsvL4s8//8zhw4d5//336dSpE506dWL+/Pls2bLFKcBcKBgCy18EVdgw1VKhtnTpUi655BJuuukmGjVqxMUXX8y7777rUqdPnz4sXbqUEydOIITgp59+Yu/evQwaNMgXw/f6yndk+926dStbtmzhqquuYuTIkfzxxx9AWdjyJ554whejrRRJ0/w9IxRwBAcFIUuCohL/xGKpaxQljGClmFJb7bVGmmbzi4alPnAKDfWgYXH0Hcg2LJIkOY1ZveWBBx5g+PDhDBw40KW8pKQESZKcQfxAt1mTZdnjVAKBjjEl9BfBKmyYajkl5E7QuzfffJN77rmHpk2bYjKZkGWZd9991zlP7i1lU0K1e+KPGDHCZX3atGnMmjWLjRs30qFDB7+ELa8M2+lTfm0/0AgN1o3Fc/OLCAkK3Af06HkrOZGdR3LjUrslZPmHjCPKoYQp/xR/b6WxbNMtSOYIkGRABkmxf8tI9mVJMiFJCpKkIEsmJNn+rQlARqmHh7yvsdoTGtaH0KCeB9K/r36VTz75hG3btrF58+YK23r16kVYWBiPP/44L7zwAkIIJk2ahKqqpKWl+WgEgYEhsPxFsHkxJeRO0Ls333yTjRs3snTpUpo3b87atWt54IEHSExMrPBGUBu8nRIqT2XZfusCSQiKDx2us/4CgZBg/a0vp7CI+FjfxBTyBz/vKQWCOHpatWf/rRiBGQSNQ2PJahZBDFvJLtAjFsqAgh5HxS6+IEtl3+eiqibgFk4WZ/vxiOoGm1p/GpbzQF5B9oGG5dixYzz88MOsXLmy0ojUcXFxfPbZZ9x333288cYbyLLMLbfcQrdu3S4o+xUwBBb3CVyto1vYhIpSS4GlpqB3RUVFPPHEEyxZssQZEKpz586kpqbyyiuv+EZgcbgBenGTqi7bb10R2r1bnfZX34SF6DfYvALv7T78SWTkKRpGCn58cKwbtce51aYmNGyaFVWzYlNL0YQNm1bK6ZxMfvl5KQTXr8eFL3Aa3daDHYljSiigkcq7LteOrVu3kpmZSbduZfcOVVVZu3Ytb731FiUlJQwaNIgDBw5w+vRpTCYT0dHRJCQkcNFFF3l5AIGFV+LXjBkzkCTJqU6H2hsvvv3227Ro0YLg4GB69uzpM2NNn3I+iPRVIBDItfx31xT0zmq1YrVa/ZrLxTkl5IWGxZHtd9OmTdx3332MGTOGXbt2+WR87iDZI8v+lQgP0TUseUXee9b4k2CLoNDHAX9lScaiBBFiDiciOIaokEbEhjUl1KK7GVtMfkw6WUc4PHVMdZSFvDzng8Dii19lwIAB7Nixg9TUVOfnkksuYfTo0aSmpqKUExYbNmxIdHQ0P/74I5mZmT7x0Awkaq1hqcy9CsqMF4cMGcLkyZPdauvTTz9lwoQJzJ49m549ezJz5kwGDx7Mnj17/BrUyjPObxWLJGr/sK8p6F1kZCRXXHEFEydOJCQkhObNm7NmzRo++OADXn31VR8dgPdxTKrK9vvOO+/4YoQ1Uhbs6a9DeKge9KrA19KAjwmxQE5B3VzjRaW68GYxn/8CizPSbT1MPZwPAoszWZoXRERE0LFjR5eysLAwYmNjneXz5s2jXbt2xMXFsWHDBh5++GHGjx9PSkqKV30HGrU6y6pyrwI9YuGkSZPo1cu96IwAr776KnfffTdjx46lffv2zJ49m9DQUN57773aDM+gCmorsDiC3n388cd07NiR5557rkJ2008++YQePXowevRo2rdvz4wZM5g2bZrPAsdJfhAYHdl+6wx7JNO/EpGh+pRQQXFgTwmFBUmUWOvmoVtSqp9zQeagGmoGPmo9xmE5L4xupbrJ1bxnzx6uu+462rVrx7PPPsuTTz7JK6+8Ugc91y210rCUd696/vnnvRpAaWkpW7duddHGyLLMwIED2bBhQ6X7lJSUuDxocnNzvRqDW5zfChYkIXl1DDVlN01ISGDevHm176AGHF7NtRW6qsv2C/4JW17hGDh/A9/VBmHTiLAb3eYWFFNYakOWJBRZQpEk5AByR40INmG11c1Dt8h64WhYHEKDybBhqRRZwmduzeU515txxowZzJgxw+f9BBoeCyzVuVfVhtOnT6OqaoUQwvHx8ezevbvSfaZPn+4SYrouyCnVyPzjDJY8q/0tuZK3ZVeHAk5lFmKzaiQkhpXbLsqldxDOclFuP1FuQdco6voF+yKgS+7OZfsHCbLVHHaZfkcKtSIQ9jlmwZ9Fe87zqDveeQnVlO3XH2HLCz96BnHoFxx5OgbKJYTmmDk062ckoTnLy39L56yfm+tDctlGhTqO/cvi6trPIwTCpnJQ68xBU5nxnlZsQ9gEskUGc9UniFu/u7BPeQnILD1LiVQWe2XhxsM8/0t+hV3szsBIgCIEM9a+SWL+KUpNFoQkISQZTZL1ZVlfRpLQZBlxzrKQZIQsgSQj7GWtjuwEYF/LLnovjjTIzmVAkhhWVMxVNsG3//w/Uv71b1r19V9CyBKr/rL147of+Xnrz0iSRJj5NM2jM4kMikKyu0pLTjdpSXePdqwjIzncpyX7Moq9TLa7UsvObbKklNVH0tuWFPt9RXb2h2O5XB/672Tf3/6fcvaLRHH+EWKDBVsP7bHbUigEW0LpmtTUbwJpakYu2zNzOV5QArLEU9sOUaBppAQFIZ9zX4QyD0MZ1/unowxnueR6j3V87AWObWVrAik7izhNLbtHO+rax/FLmhUhBa47//mGRwJLTe5VdcXkyZOZMGGCcz03N5dmzZr5tc+NZ0ooTC+CP7M93nfv7rO+H1A1nAozs7TT20jCIejISDJcarm4TsfhS7wNbV9Ttl9/hC0373gLc3AppUW6HUeK2YIqLHD6uN1zwOHyqN8Fhf32KRwPU5x317J6SM59nQ9dxz729bI3Ose6vj2m5BhC2Dho7u4UcGVFRthUZEUGRan2XdBlWq7yRadAkBTWGJum0rpFK04UWGkXHA+yCVUTaJpA1XSDTU0IVNX+bVNp89VRCmMSyO59FWgqQlVBE/qyEKCqoGkITQNN3yZpGmgaCM1lWS5n8G1SFHtyN6HvK8onkIMGwoZNzifxt5McWfqJXwWW5IRkisKLIBdsuTYQ0KT5TiIsRygtjaQswZ0ASRdsJamcUCpV9Y19ue64NFH/lJYL91EE/FryAb1SLvNLnzfN3Yg1VxeGtUgz/5eTo2/Iy/NLfzUR9GMakrUqzakhrPgSjwQWd9yrFA9Vgw0bNkRRFDIyMlzKMzIySEio3O0vKCjIJapfnRBiolOvhvQY2qL8c6Lc3dp1ykWSQLVqCCTMQXLZ9vISunO93LJcXn7H+dYqBAhNuKy7bBMCocH/fbiEsEORbLt9u79/kTqlLHDc+TOlIoRMgbk3YVOXA1DfEwClL4+gkWrl9sfuqeeRVI5mVdnzMjS9cQzd/327T9tu52a91Zd3pqTUvx5NTWOa8uKjL7qULVw7gqwSCzcP3VrrdjVNQwiBpqkIoaJpKqpqcy4LoQICTbOha4g1hFDt15SGpqmAZi8/pwyBcO6n7yPQEJpKnlWgmEwIoXI27xglZ2ZQXJJV6+OoCVuxSrsOcTzevxVmRaIQKFQ1GpkVNM0+2jLltV2ZXaYjdMwkOYRVR1JjgXAuU35dlFsvV/+no0dZhInnLo8gKSrafi8W9j70SgfOltCysaud5/lN/U7DeSSwONyryjN27Fjatm3L448/7rGwArrnRvfu3fnhhx+47rrrAP3C++GHH3jwwQc9bs9fyIpEUISFkJj60yy5gxSsImvnfwTNCkjn9XxWgBA4NiOV4hxePd4UFRlVrfs0Avp0jXfH7QgrUJv7sK84knmI/Wf8a0shVEHTBiH0b+Yb27LaUpJ1jEX50KtTK9o0rvzl2vsIVIFG/d5DPHoKONyryn/Oda9KT08nNTXVxXgxNTWVrKwyiXvAgAG89dZbzvUJEybw7rvvMn/+fP7880/uu+8+CgoKGDvWnSBOdYTknUuqqqpMmTKF5ORkQkJCaNmyJc8995xLm0IInn76aRo3bkxISAgDBw5k3759ng3TBLJ64Qks/vAS8hdFS9+k4JWbURTreR27p84JgN9KkyRQ616LJ8lmZM4f7WHV+Pd/WGhVQRVEhdR/zFOHpkYKIONxf+KLKOPe4vPX1tmzZ3PxxRdz9913A7rx4sUXX8zSpUuddRwR+RzcfPPNvPLKKzz99NN07dqV1NRUli9fXsEQtz6RJMmra/HFF19k1qxZvPXWW/z555+8+OKLvPTSS7z55pvOOi+99BJvvPEGs2fPZtOmTYSFhTF48GCKPXAJlUxcmBoWO+fDlJDy8zRCclYghITUpEt9D+ccPD+J165dy4gRI0hMTESSJL788ssKdf7880+uvfZaoqKiCAsLo0ePHhw9etSzjqQKC3WOUCTdDsYDpk+fTo8ePYiIiKBRo0Zcd911FQIt1oQsmagbB1j/UnYE/vkfnsjT74WxofU9wapPPYFuHv2XwZfpp2uB12Lque5V7hgvHj58uELZgw8+GFBTQOciSd69AP7yyy+MHDnSGbq+RYsWfPzxx86IvkIIZs6cyVNPPcXIkSMB+OCDD4iPj+fLL7/k73//u1v9KCYJSTMhhHAaql4IOEPz1/M43KUoqCdhz62o8QJTVZWpU6eyYMEC0tPTSUxM5Pbbb+epp55y8TiojJdeeomJEye6PabahggvKCigS5cu3HHHHdxwww0Vth84cIC+ffty55138swzzxAZGckff/zhuWF+AGhYhCzpRrsesGbNGh544AF69OiBzWbjiSeeYNCgQezatYuwsDC32pAlE/J5c3ZXg5//h2kFuodVXFj9CyyOQ/XVfTbjUBpfvvw6mmqlvCVNuR5plNyWGyf5JraVxwhR3/KKkUvIbSSnGWyt6NOnD3PmzGHv3r20adOG3377jfXr1zsjwR46dIj09HSXvDtRUVH07NmTDRs2uC2wyCYJgYymCRQlcAWWvJzjPPblDeRp1jLDOEBzLEv6suPRcQyb/tJWUj+eAJ7j3rni0LzNnz+fDh06sGXLFsaOHUtUVBTjxuk5a87NuLps2TLuvPNObrzxRg/HVDst4dChQxk6dGiV25988kmGDRvGSy+95Cxr2bKlx/0EgoCtyRKonv1Iy5cvd1l///33adSoEVu3bnU7W7ksm30msLgjBJfnX//6F++88w6vvfaaS5qV2uCYNkjNyOPs3gxCzDLhZhNhFoVwi0KExUSEWSHUpNTq/53uEFhC6z/onmM6X/bRebt/yw7yz+wkLKY1smIup3CUEAIKc9I4tvNnoJ4ElgDAEFjcRJLAm9mISZMmkZubS9u2bVEUBVVVmTZtmjNabHp6OkCl8Wgc29wap/0/+s3aH1HMegwGGZkD2fvp0DyF/h371P4gqkFoGj9tfIW8Yt1WSbfKd5rj2y3uHRFmBMfzjrOeIrooobQ0RyJTFmvGoWJ1RIeQkLg0v5CSX8+Qcm39P9TOpXjNItQ/1zq14EGy+0abNWnegArecl999RVXXnmlx4nNZDULNN96wGiaxrfffstjjz3G4MGD2b59O8nJyUyePNlpRO82AaBhQfJcw3IuOXY3W08CDvpSYHFHCHawZMkSNm7cSGJiok/6Lra/YqzYnsafP2ypsp5weETKEpIiIckSsqJ/FEVGUSRMiozJJGNSJMyKjMUks+7R67FmZXCdq5MV999/P2+//Tb9+/dnzZo1LtvuvfdeZs+e7ZPjK4/jLDn+5x604yeQZRlZkZFkBUmW9XVZQVLksnVFX5dlBVmWkRQZNAnZpGC1B0Md9fSTxDRuWKG/hU//l8yDv6FpWj1mYT7Pp4T+KkiS5JXR7aJFi/joo49YuHAhHTp0IDU1lUceeYTExETGjBnjs3HGNogmAzj+qeOEVu2f5mwxnab/W1Xv6w2Hj67j4X0ferRPjKrxzrUfEtawTY11S7at5uB/7yN4ZOCdstJ34wkLKRcULQhKYpLd2rcmzdu5ZGRk8O233zJ//nyPx2ku+t3jfWoiMzOT/Px8ZsyYwfPPP8+LL77I8uXLueGGG/jpp5+44oorfN6nPxGyhORFBFVN03jkkUe47LLLKuR/qQ5ZMhOmCFTNhiJ7d467IwQDnDhxgoceeogVK1Y463qL2aI/0K4f2Iq7wy+h0KpSaFUpsqoU2zSX5WKbSrFVo8SmUmLTKC33sdo0bKr+KSkRqKqGqgma3PsGZpPEd3f1JtxiYufOnVx99dXcdNNNzjHcfffdPPvss8710NBQnxzbuZQUADL8OPcNwooLfNau2VJ57BZZllGtWbx2y7kJDSsTIqoSLCoLoFSxbmUxl4RmJTKuVRXt1g2Bd/cPULy0uWXixIlMmjTJObXTqVMnjhw5wvTp0xkzZozzLTojI4PGjRs798vIyKBr165u93Nj/yFktj1LidWKJlQ0oaFJGh8v/ZbIXf5LNV5q0y/YDy5+jI7tbrRHxcQZLVOS7ToUSaql6t9uwxKA4bglVAqUSwmbshLQtU1hbr4B1aR5O5f58+cTERFRqS1JTaiWZki2fJ9a2juycY8cOZLx48cD0LVrV3755Rdmz57tkcDi/N/W40ucLrDUXsPywAMPsHPnTtavX+/RflHhLbHlwtGsLSQ3dD8PW2W4IwRrmsatt97KxIkT6dChg1f9lceRvbltw3D6JfnfaWLGjBm0bNnS5TwLDQ2tMoaXLwmKiIeCLHr9bRKNgmU9Do5mj2NjX9Y0e6BDIdCEWq68rO6po7mYzBKRDYOIiG1ARGxUpf0NuOMWtq9oitBEuZgvZYFjyuJzldNmO4N5lYtFI86N51UWy6usHUedsvLTx/IIbdDDj79ozRgCi7tIEt54HRYWFlZQ4ymK4rzhJycnk5CQwA8//OAUUHJzc9m0aRP33XefB8OUiG9cURXdrFEiebv8p0YU9uOwmIIxm33/RuN0HQyEaYMKCJDLPLMkD9S1nmre3nvvPUaPHl2rSNO2oDZI5Pg0gF3Dhg0xmUy0b9/epbxdu3YeP7SdUzH1pu6undGtgwcffJBvvvmGtWvX0rRpU4/2jYvsTNpJOJ13yGuBxR0h+MUXX8RkMlWYIvIWzWnX4f//YWlpKQsWLGDChAkuL0EfffQRCxYsICEhgREjRjBlyhS/aFkcd6IOfVNo0sA/WpzyxCUlMOjum2qu6Cc+nfYrQWGVC1N1hSGwuInkZRyWESNGMG3aNJKSkujQoQPbt2/n1Vdf5Y477rC3L/HII4/w/PPP07p1a5KTk5kyZQqJiYme2wJUNn4ZEBJH0raB0BDCZg9hrtpDlasITbUb6tjDngvNXuZ4S1DL6qM5yzZs2cVbcz5l98FTdMwexZIlS3wyZtcDcOolfduuD5AkDWqpxq9J81aedevWsWfPHj799NNaDlTG135WFouFHj16VHDj3bt3L82bN/eoLce/tjbPuhMnTvD444+zbNkyCgsLadWqFfPmzeOSSy4BdE3l448/zvfff092djb9+vXjzTffpHXr1q5jkOWyABvujlsIHnroIZYsWcLq1atJTnZvOrA8qtDtF8xKiMf7nktNQvDWrVt5/fXX2bZtm88NnR3JEOvC1ffLL78kOzvbJdfXP/7xD5o3b05iYiK///47jz/+OHv27OGLL77wef+Os8RXRrfVsXbtWl5++WW2bt1KWlpatfdYXxpRBxqGwOIm3qYJf/PNN5kyZQr3338/mZmZJCYmcu+99/L000876zz22GMUFBRwzz33kJ2dTd++fVm+fLlP8jZZCk4hE8E1K8b4XOWetzePwiY2Ei9L5OibHsbecJsA17BItbuUatK8lWfu3Ll0796dLl1qGdtFkmsl8OXn5zsDQYLu0ZaamkpMTAxJSUlMnDiRm2++mX79+nHllVeyfPlyvv766wohD2pCOAK2efgAOHv2LJdddhlXXnkly5YtIy4ujn379tGggR4SXQjBddddh9ls5quvviIyMpJXX32VgQMHVnA9FrLkkoPIHR544AEWLlzIV199RUREhNNIPioqipAQ9wSQUpseX8SkeO/9UpMQvG7dOjIzM0lKSnLuo6oq//73v5k5c2alYSfcRbOroeviIT537lyGDh3qYjB8zz1laSc6depE48aNGTBgAAcOHKiV51p1ON2afdpq5dQUWsCBr42oAw1DYHEXCY/fvMoTERHBzJkzmTlzZtVdSBLPPvusi8GYr+gUFM0aYG7K3Ugmsz5t4czoqmdfRS6XxVVS9GkOyZHBVS93lsl6ZlYkCcvVQViUYCJDG9LoTc9U4W4TwFNCkiSgluHQa9K8OcjNzeWzzz7jv//9rzcjpTYali1btnDllVc61x2JR8eMGcP777/P9ddfz+zZs5k+fTrjxo0jJSWFzz//nL59+3rUjyi16aP08Ld88cUXadasGfPmzXOWlddy7Nu3j40bN7Jz506nvcasWbNISEjg448/5q677iobQy2MbmfNmgVA//79Xco9yfRtVXXvLYsPNCw1CcG33nqrS/gEgMGDB3Prrbd6HV3c166+VXHkyBFWrVpVo+akZ8+eAOzfv9/3Aov9uy6icNcUWgD8Y0QdaBgCi5t4GziuvjHZbSy6X3wPSj1m2q41jnmCAJwS0m1YancpuaN5A/jkk08QQnDLLbd4MU6J2hhi9e/fv8bp0DvuuKOCkOUpwqq7g0tVeElUxdKlSxk8eDA33XQTa9asoUmTJtx///3OaNsldnfR8ppKWZYJCgpi/fr1LgJLSE4xBQ3dC/bmHLcPbgxWVdewWEzeCyw1CcGxsbHExsa67GM2m0lISCAlJcWrvjX79Sn5Poi6C/PmzaNRo0Y1PphTU1MBXBwZLkT8ZUQdaBgCi5t4OyVUp6g2+4Nd2C3AHdlXQai2+h1bbZFlTjXsQs76fEzHv3ZxydNf5iS7TFMulbYklWXHlh1eS47M2JJLudUGx9OgSYJESKiEJMnYHZyQZd2zSZIdH/0NUpL1vpJMAq04jayt64nqfCmK2X2zVnc0b6Crusuru2uFJAe01C1K7QKL2TOB5eDBg8yaNYsJEybwxBNPsHnzZsaNG4fFYmHMmDG0bduWpKQkJk+ezDvvvENYWBivvfYax48fdwnKl5t2hMSjBZzK9p2LqrvYNP3YzYr3LxPuCsH+wBmu3o8aFk3TmDdvHmPGjMFkKnuEHThwgIULFzJs2DBiY2P5/fffGT9+PP369aNz586+H4f9WAMhMpS/jKgDDUNgcRM9cFzg3uwdZG78mW8+SMcmgtGEjIYJgQLYU5yfp1mPi6VQdnS8B4qAP/3Xz4mT5WPuusdtcTFEWH+Dr4dz9I8pJN32qN/G5xV+MLr1JaLEIbB4dlvSNI1LLrmEF154AYCLL76YnTt3Mnv2bMaMGYPZbOaLL77gzjvvJCYmBkVRGDhwIEOHDnXRjoRE6cG64nJh2Z3DibrpRtpeOoSYGP/bA6iaDQkwexmDBdwXgsvjjd1KeRxGt/6cEVq1ahVHjx6toNGzWCysWrWKmTNnUlBQQLNmzbjxxht56qmn/DIOpw1LPd9S/WlEHWgYAou7nCcnQs7JMxRpDeh1yVnMQQqyUoIsS8gyhDeKwuSnIEr+RgrRxz3ib9BswJX2OAH2jyZAs5v7OdaFvcwRq6BcmTPegFbWhtAEtlJVV7jY1/WPZnemssdXUPUYBZpq346gsGQhtiiZ0M+uRSrNra+fqGak2k0J1RWaXcOChxqWxo0bV+pW/fnnnzvXu3fvTmpqKjk5OZSWlhIXF0fPnj2dXkQA5tAw1FefQpnwPC1+Pgg/v8zP/RYzYs53tT8oN1FRMQGydJ4nLrVrchU/HsegQYMqnYZr1qxZhSi3/sTpJVTP2Zr9aUQdaBgCywWHfvF0GjUAS2R0/Q7FhzjfHOxJHSt7k6hv3VHx4kDPuCsjBaQNkB2bPl0pe2jDctlll7ntVh0VpceR2LdvH1u2bOG5555z2d5x2GhKr7yerJMHybj2JlqtPcSJ3Vtp0ra7R2PyFIdBbF3EL/EnjjRMF/qbPpTZLin1PCnkTyPqQMMQWC406iFeSU1ur74lcAUCgYwUwBoMbxN4+hthtdtXmTx7Ox8/fjx9+vThhRdeYNSoUfz666/MmTOHOXPmOOt89tlnxMXFkZSUxI4dO3j44Ye57rrrGDRoUIX2LCGhJLTsSPbcVxG3T2DHa8/Q5J2lXh1bTQiHO3C9i93e4TC6rYs4LPVNWbZm//dV0z3WX0bUgYYhsFyo1KFxZU1ur74h8G+AQgT2lIuugwpggcU+JSR7aMPSo0cPlixZwuTJk3n22WdJTk5m5syZLpFd09LSmDBhgjP1xW233caUKVOqbbdtr6Esa/U0UnGJ5wfjIQ6vGhHA/x93EHUYh6W+cRrd1sGx1s09NvAxBJYLjXJTJ3WFO26vfwUEUkBPCYkAt2Fxalg8tGEBuOaaa7jmmmuq3D5u3LhaeVAIWQZV9Xg/T3E84LUAPn/coSzS7fmtKfKEupjF8/Qe6ze7lXo+Pf86Z9VfhXoQWOoSb45q1qxZdO7cmcjISCIjI+nduzfLli0DICsri4ceeoiUlBRCQkJISkpi3Lhx5OTkeDC2wJ4SkgLdrdlhw2IKnPcob3ILeYIj+JgIZBsjNxCi7rQO9Y3DabSebW7rjED4nwbOncHAR1zYAos3NG3alBkzZtC6dWuEEMyfP5+RI0eyfft2hBCcPHmSV155hfbt23PkyBH+9a9/cfLkSRYvXuxW+4EusAT8lJBTwxJAt6Va5BaqVTf213TtPL9uNaFroxT5PPd2cgNnVN/zYLr6QiGA7gyBTUaJlWNb0/l5/2lAV/+7dZ6ec/+REC6yRLVNVNjXdVNwUjiP3tX1nEqBmySwvhkxYoTL+rRp05g1axYbN27kzjvvdHGDbdmyJdOmTeOf//wnNpvNJUBVVWiaRFGu/+0dak2ATwk5vIQ8jcPiTyRJIvJgJl+MuRIsZoTFTLOrruHSG93PoO5eP/r3eS+waKUAKJIvc4L7jw9PnCatxEqIImOWJJ4/eBLQNV72+JL6t+NjDzgpAaJEAxO89PIm3VPIGa+yXMDKckEqpXKN6Yv2epJEYqcYRg/0beqAC5HAuTMEONtTQog6Bg0tJqTy95TKbjA1qM6EJwJ5FXWLTxZi2pVdzQ7n943P36iqymeffUZBQQG9e/eutE5OTg6RkZFuCSsASDImz80v6gxRhxqWtNRT5KXl2/NSgaSUixYsAfaowbIsIZtkJFmi9LA+/Zaz9wjFsoJkMSGbzCgWM7LFjGwxIZtNKBYzUpAZRfH/jHb00GEUfbOc6NPFyKX5hJ8qoGD/fPCxwCJLCnps6gAWKN1AiFIkQPFBEkd/c7K4lIl7j1coD1NkWoYEoeekF3qYJ6H/Z4QQqMJxFWmknCxBCdafCQLhDPmEva4ebFzTdzin3LEectbKvqxiMASWGjEEFjf5Nh5aJUezvme7+h4KAK998DulqVkVyp2y0nn+plYlXh7Wjh076N27N8XFxYSHh7NkyZIKQccATp8+zXPPPedROHxZkYjK+YWMVYt1uwdNRagakiITe/kITGHhHo3VdvBP1P3bAccNzh7oDq3s/ysAodrXHXdSoWvYnPX1b/OZVWiqhTOf7rFvw566wd6hKN+P/caqAZq9jjNY37llrvsITfDV3lw8NVWNzDnFJUDOMxPcql96zVi6vPKYh714Rp+7noS7nnSuL7prIAlHfB8c0GnDcp6/aGhaKQpgkgNYcrdzqEjXht7TtCHD46IpUFUkJK5oEF4heaQ/efnd7QTtD+CAkwGEIbB4wP7CwFH3V6mkMaaEqiUlJcUZ8XTx4sWMGTOGNWvWuAgtubm5DB8+nPbt2zN16lS32y6RY2lo20Hk+jsrbDtZlE/i9Xd7NFax8A6CbLs82qcmirUrKdqe6fmO555wUrlCybVcEwIV6N4hhvbXXYRQ0aMOa2UCDVpZtGBNFQhVoNraoaa1RZJtCKsNzWpF2GwIq4pmsyGsVjSbDWwq4t0Z2I4ersUv4B2y7F/DZbUODHz9idDshtNy4E8JnbSngugaGUrPaM9eJnyNMOxg3MIQWNzk4ohQ2oUHTpbj0qJCgos15k74rOydTIBqlYEGLH1mHLJss99bBWgqNpuKkJSy3ICOy6SSdV2tK9H//ilEtetTl4dWBY4oTd61YrFYaNWqFaCHa9+8eTOvv/4677zzDgB5eXkMGTKEiIgIlixZgtkDF9uofy8nLzMdSVGQFQUU/bcOmdWexN8eRUt9CpfkjC4HVPatT91ImMmmJHo4pn++BpLsTPKoz7GUn1xXyspwJGUsVyZJuu+lJBEqSYRK/rX4L8kpgcd/JjwxjMhmkR7u7V5W3S3fLEWyZ3euUxTFL15DsiShcgFoWHAILIH/aEkr1s+fpkH1LFxpAmH467pF4J9VAUIAeHS5kBWaS0mcmQ55UtkzXAKT2UaQ2IHJpOlurI5NkgmLqQRTSJj+QLTPuQokfVrA6VZp1/irGvtPFtEmdX2ACCwOfPuP0DSNkhJdc5abm8vgwYMJCgpi6dKlBAd7JqCaw8IxJ7eqUJ7e421K0g8RdTAbgOC29kSUjukafcX+5ciyrWFFQ7nsHygN4yvtb+rUqTzzzDMuZSkpKezevRuA4uJi/j1hAp988gklJSUMHjyY//3vf8THV96er7AW6w8tk9mPd2GzGWEt9V/7VSDLisdeQ7nWIgqspWhCQxMaNqEihMBmn8qzCZWMojxigLOFaZhNUS5nhWt0H8fUkYSmFqGqeZiDGiMhI0mKPW2FYl+X9azi9jZke0oLCbumCz0yrbCLScKZ3Z2ydYSzrmO5bGRlo9G/BEWlZ7EApvMgJ1KGPVBhUojnAkteXh5TpkxhyZIlZGZmcvHFF/P666/To0cPj9sSmtDd5w1qxBBYPCCQTqmSBoKlvSy8NvRvVdR42Kv2rblneOPuMViLCig5dQz9FimQ0Owv9/Z1+9SThABZgZiL/CPd+SAO9uTJkxk6dChJSUnk5eWxcOFCVq9ezYoVK8jNzWXQoEEUFhayYMECcnNzyc3V55Xj4uJQlNrfgBOG/xOA45PWUdAolJRbfZeXpkOHDqxatcq5Xt5AePz48Xz77bd89tlnREVF8eCDD3LDDTfw888/+6z/ylBL7K6tFt8/tH4bPx1lxSeEaaUUJF9S8w4+RlIUj6aEtp0+wrDfM0GqXlPXRjTjP8DpvQ9y2ssx1idRgA2Fo8UQ4wOF9H0b57Nu3/8wmWKIj+7C670fpm20e1q4mjhjd6NvVAuvtLvuuoudO3fy4YcfkpiYyIIFCxg4cCC7du2iSZMmHrWlaSKwHi4BjCGwuEmg2bDKknMCx6dUeGtf9C1xEf/l8aH9a9xXkTSuHz2M5iMe9Pm4yjQRtT/mzMxMbrvtNtLS0oiKiqJz586sWLGCq6++mtWrV7Np0yYA55SRg0OHDtGiRYta9+ugGNB8/BA3mUwkJCRUKM/JyWHu3LksXLiQq666CoB58+bRrl07Nm7cSK9evbzuW9M03bbXpiFUDU3VNXMFmUUAKEG+F1hsx46iWiIwDbmRJlf39Xn7NSFLMpIHU0LHi3JAMnN1aBop4WFIkoQiyXa3WQlZkpElkEUyJ8R/iQsuy6bu1KU4ZfVztBulGXz9wad88/luMo5nANC8dTP+8eDf6NGvC+nH07n9qspfXB6f+RCXD+1l70cqc7F16mPsW5wvCOcmHC0/lVmm9dmcU8A32WZeFTX/762ayqG8M+zNPcmh/AyO52eSXpjJ6aJT5BafIr84E1vJYaKjLiEnZwsnM49z01ffEhHZHQkZWVJ0LZJsQka2/5YKsiSjyCZsQiLf3JKwqMuRJQlF0hMVKhIoksSugiIk8NjAtqioiM8//5yvvvqKfv36Afp98+uvv2bWrFk8//zzHrUnNPHXiT7nJYbA4iYO1Wyg4M+RON7a0zd+Q8HpNBRFIToi3D59pLtlC2HXsZRzTFn5xTJWfvY1oUu/1dXI5zi3VOJUQk6xfgpGWPS3HaeTi30sDkFRk8Ih/H4K8gprfVxz586tcludpBcQ+Fz1u2/fPhITEwkODqZ3795Mnz6dpKQktm7ditVqdcni2rZtW5KSktiwYUOtBJY93xzkp28Po5X7/1RHULgfPEUsFiSLhZjLexCWnOj79mtAMpk8mhLSH4YaIxsn8bekDj4fT3GvWIZcprgEQ3z2vpfZvn07A/vdyOC0US7158yZw8svv8xTd75AeLjvDU13HkrnUHYap4vPsuzEIQ7mpXM0P4P0Al0QySk+TWHpGazWswjbWaRzfMmEHInJ3ICQoIYkRLUnpcFInu92C70/1s/XqKieSEhoQsWmlSKEhkDVv4WGJlQEGkKo2EpPYZO3ctTUA6fDePl7C9DI4vkj0GazoapqhSnjkJAQ1q9f73F7QhM0yCzl+SnrdMFFkvQYj5JEQpdY7rq2/hMYqjaNgpz6dzoxBBY/UJNtwb333suqVas4efIk4eHh9OnThxdffJG2bdu63YckSfbcML7H8daecN1dHu1XeGI32acykSR9/lyPuVHJR7a/1ckSv/6WQWJ8OEmJkWVGoxJlRqaS/vaXV2hm7wkoVGL8cch1giMQla/o2bMn77//PikpKaSlpfHMM89w+eWXs3PnTtLT07FYLERHR7vsEx8fT3p6eq36O3siHyHg0p7xzhgqkgySLNvX7fFWZJmgMBONL27kg6N0RWkQQ1BuGnn/vpuMmGS6//Kdz/uoDllWkDwQbBVnBFv/eP9UFwyxQ4cOFbRvS5YsYdSoUT4XVvbkpHPTt6PRbGdpKARPHLO5VpDDMZljCLHE0ii8BTHBl9AoNI6m4fG0CE+gVUQ8bSIbE2qu3J5kx5gdHo9p5PLHOJy9h2P9u9TmkKokIiKC3r1789xzz9GuXTvi4+P5+OOP2bBhQwXtrDv06NeMraYTKAKEarcT0gSm40Wkp56GOhBYNFXDVqphCSkTCYoLrOzfmsnB7Zmc2JuNpgrCouvXQNkQWNxEIDx62FRnW9C9e3dGjx5NUlISWVlZTJ06lUGDBnHo0CG3bSX8GQKsqrf2mug14U2P+7rczXon9x1j73/3gXIen7ISPtXiDB061LncuXNnevbsSfPmzVm0aBEhISE+66c8sgSXjK1ZUzB16lSeGVS10J6ens7EiRNZuXIleXl5pKSk8OSTT3LjjTdW227H/07izNbrOP7Gu8gHdtb+QGqJJCtIHmhYHAKLrQ7CDNQUDHHr1q2kpqby9ttv+7zvfbnpCGsmEtC+xb/o1qAJyREJtI5sTJvIBMLMdR9IrshWiEkJrbliLfjwww+54447aNKkCYqi0K1bN2655Ra2bt3qcVtXX9yYqy+uaJfz/EsbETb/nTd5WcXsWH2czMO5ZBzOxVaq0ebSeLoPbUH6wRx++Xw/pcUqia2i6HNjKxq3jCIuKcJv43GH8/juX7d4+pipyrYAcAlG1qJFC55//nm6dOnC4cOHadnSvWiHklQ2d+xLqntrj4io35MVzn+3T38+t6Kjo2nTpg379+/n6quvprS0lOzsbBctS0ZGRpXnZU0ID20DqxPab7vtNrKzs1m6dCkNGzZk4cKFjBo1ii1btnDxxRdX2aYpOIj4y7pwckEs7PN/FuVzkU0mjwQWUx3kCHI3GOLcuXNp164dffr43uvvmmZdmRKcTIPQJD694gGft18bim0FWEz+EdxbtmzJmjVrKCgoIDc3l8aNG3PzzTdz0UUX+awPYdOQTP7xtDvyxxlWzv0DJGjSugGXjrgISYJtK46w91fdHqptrwR6Xd+SsKjAiVpsCCwe4MnN2l0tRUFBAfPmzSM5OZlmzZp5NBaPQvy7SXVv7XfeWTEgmoFnCD8m0svPz+fAgQPceuutdO/eHbPZzA8//ODUWuzZs4ejR49WmYqgRjx86FYntP/yyy/MmjWLSy+9FICnnnqK1157ja1bt1YrsDjQvXXqXmCRLBYUm/u/w6miU0AY+7L3A139MiZ3giEWFRWxcOFCpkyZ4pcxgD10ghI4sapKbEWEBDX0ax9hYWGEhYVx9uxZVqxYwUsvveSzto8EbSTT9Dt3vP8hJtmMSTLpH1n/KJKif8uKc/nq9ldySZvOVbYphGDvpnR+/HA3zdrHMPD29gSHldmate+byMl92UTEBBPbpH6D6VWGIbC4iwf3ane0FP/73/947LHHKCgoICUlhZUrV2KxuD8/6C8voXMp/9Zevzgi+NbvKLxBSPg08++jjz7KiBEjaN68OSdPnuQ///kPiqJwyy23EBUVxZ133smECROIiYkhMjKShx56iN69e/vEQ8gdqhPa+/Tpw6effsrw4cOJjo5m0aJFFBcX079/f/caNykeeev4CslsxqS6/z8MN4fZv/13868pGCLA4sWLKSws5LbbbvPbOFS1mGA/aTRqg1UtJMYU5pe2V6xYgRCClJQU9u/fz8SJE2nbti1jx471WR+7Gq7DpmaiWhNQsWHDhooNDRUNFVVS0dDQ7N8lpkLWrP+BVy2vIMu6p5QiSyiyCVmW0ayC379N5+i2bNr2SqD/rW0r5OOyBJto0cm/Qp43GAKLm3jymHFHSzF69Giuvvpq0tLSeOWVVxg1ahQ///yz28HKnJk//Uz5t3YD71AlCay+e8geP36cW265hTNnzhAXF0ffvn3ZuHEjcXFxALz22mvIssyNN97oEjiutniiYKlJaF+0aBE333wzsbGxmEwmQkNDWbJkidtGi7LJVD8aFrMZxYNuoyxRQA6xIfpDIC8vj4wTaSQmNSM01D8P9/LBEB3MnTuXa6+91nlu+KffYkICSGBR1SJCzf4RWHJycpg8eTLHjx8nJiaGG2+8kWnTpnkUGbsmioKsNIm+gm+GvOBW/Zvn3s4u01ZuXlO1HVioHMG8Oz6l/aXua/MDCUNgcROBrtWoDZVpKaKiooiKiqJ169b06tWLBg0asGTJEm655Ra32pTwT/6J6t7aDbxDlSQkHxrRffLJJ9VuDw4O5u233/apkaW7l0BNQvuUKVPIzs5m1apVNGzYkC+//JJRo0axbt06OnXqVPM4TCbQ6sGGxWzBVEm3JYX5nDq2l6wT+8k9eYSitBNYMzMoyjzLK9kRxOeeZXP2WcKLdLf8FSOu4/qXp3s9nuqCITrYv38/a9eu5bvv/OtRJbSSgBJYNK2Iw1mp/G3Vs5hkExbFglk2E2IKZmKnv9E8PLbWbY8aNYpRo0bVXNELVK2UYJP7U2wzr3uR3w/tQtUEqlDRNK3ct0Z26VneOv4K9xy8heVdlxNuCbwpn5owBBY30UTtxYOatBR6zBJR4a2oOmQJv7g11/TWXl/4M/dNXSFMEpK17h+yvsIbu9HyQvuBAwd466232LlzJx066B5HXbp0Yd26dbz99tvMnj272rbUUiuqJmESKrasLOSgIJAkpweWcAT+0TRQVeeysH+wf4SmudRzJmYUmn3qTt+mFhRgPXSY0qNHifk5FUXAt3cMQcnKJTi7kLCcUsKL9L7NQCxQqkBupIISHUJRg3CyW3enKL4xIfGNCH5nls+mBqsLhujgvffeo2nTpgwaNMgnfVaJKCHEgwesv0mMuZSM3N3szVyPEFYQNoSwImkFhCrBvHTpmPoeIgAPbniH/Wf32QPfOYIJymi2sx4JLI1j42kcW33ajUPr/uTbg99y1/d38d7g9wg1+8eLyl8YAoub6IHj3KM6LcXBgwf59NNPGTRoEHFxcRw/fpwZM2YQEhLCsGHD3B6Pv4LY1fTWbuAFZgWpxFZzvUDFC4mlvNBeWKhrGc6NMKooCloNdim7PvyBn9YLGqcV0E4tZV+fy2o9ptoQDNgUMB8/RWlsOHlNW1AY15Dc+MaEN25GVJNk4pLa0KBhsyojqK57ZxaSj+KgVBcM0cELL7zACy+4N61QW6yaiiRKCAugB+CKYa9WKMsuLebyj3sQbAqcbNKr972LpERgMkUBmj13k0CxxHNZgu/SeADMuHwGt3e4nduX386ENRN486o3Mct+CPDoJ7wSWGbMmMHkyZN5+OGHmTlzJmBPuPbvf3uUcO32229n/vz5LmWDBw9m+fLl3gzPp+hTQu7VrU5LYbVaWbduHTNnzuTs2bPEx8fTr18/fvnlFxo1cj/QVl0Z3foLIQQfzX6bnNw8HBZCwhECF0ciNlA1lRKrlVJNQo038efBFCy/RhIaHkJYeAjhkaGEhYegmAI/2RpBCnJhPWQY9hUeyCvVCe3R0dG0atWKe++9l1deeYXY2Fi+/PJLVq5cyTfffFNtu7knspFEJM2v6oTSZSwR5mKEPYmd5LhAHS7/etx1HBmuJVm2RxG1ByW0r0uS7IwwKjnqy5JzPzkkGHNSc4JatcKcpAsiNU9aVU1IYSFyAIQI8CU5pXo6hjAPp4RatGjBkSNHKpTff//9vP3227WO11MVZ0ryAYj0k22Lp6h2QW9w64d4pY40Pm1j2jLzypnc/f3d9P24LytuXEF0cHSd9O0ttRZYNm/ezDvvvEPnzq4uVLVNuDZkyBDmzZvnXA8KChzfb9A1uO6KB9VpKRITE30yl3y+z5AU5eexP+M0JtWKyX4w+lfZgUkSSJJMaHAI0ZYg0vLyOZi1m4Pf7a627RApCkU2YzaZMZssWMxBWIIsBAUFERwcRHBIMCEhIYSGBxMapgs94RGhhEeHYbH4721DClKQ/ejW7G88icNS09Tid999x6RJkxgxYgT5+fm0atWK+fPn16hl1DPbKlj/3EGbd/znousvNFUlpLgI5QITWLJKCwAI91DDsnnzZlS1bJp0586dXH311dx0001A7eP1VMWevGwAikVgaBXO2gU9T383b+nVuBcfDv2QW5fdyuJ9i7mrk2dRzeuLWgks+fn5jB49mnfffdcl0ZM3CdeCgoJqHdCqLhCIgMolZA+bWt+DqDUO1X+Pi7sy+Iab3NonP7eQ7NO5FBQUUZhXRGFRMUUFRRQXlZCdm8X+E3+QENUcSciUWkux2qyUFOWiFthQhRXN7hLozCZXGUJGxoSCCUU2Y5J1ocds1gWf4KAggoKDCQ4OIjQshNCwEBKbNaJ565oztMpm2aOw7oGGbiPi3jVQ09Ri69at+fzzzz0eQ3RSDByFrdJl1I1ztm8pKihEFgJLxPln8FgdZ0t0gSXCwwfvubZxM2bMoGXLllxxxRWA9/F6zuVksa5hCQoQ4+Asu8bH09/NF3Rt1JXBLQaz6siqC1tgeeCBBxg+fDgDBw50EVi8Sbi2evVqGjVqRIMGDbjqqqt4/vnniY2t3Iq7pKTExUA1Nze3NofhEYLA0mpIknR+xyRxahrc/1HDI0MJj6zuwv5bjW1omkZJsZW8nAIKcgspyCuioKCQooJiigqLKCoqpri4mJKSEkpLS+yCTyklhUXYNKtT8BGS/lYYmZNDp993cEpuqBtrCg3JYbQp7Aae9rIgm4okBHt646yL3fBTCLsBqKNcOMrtxp/ltwOx9z5Go4fr1tU8P99KST1riNr98ypO/PExRzMCxwbBE/KyswEIusA0LIcL9Xuwldr/X0pLS1mwYAETJkxwGtl7Ha/nHCIlffqwc1SDWo/TlzgElihL/UxRXZpwKSsOr+D3U7/TOa7qgHOBgscCyyeffMK2bdvYvHlzhW21Tbg2ZMgQbrjhBpKTkzlw4ABPPPEEQ4cOZcOGDZXm1pk+fXqF5IL+xiYE5kCSWM5zHCH269r7R5ZlQkKDCAkNgsa1T6Ros6kU5Bay/amnaZieTnFyE5BkkGWELNvtJGSEouiJKmUZmw2CzArmELNuKyHLSLKi21IojnXZuQ1ZsS9LdnsLGUmRyf70XUoOV5z39zWqTaMws5D8jEIKMos4eCzf7326Q2GBRrBU7JO2KrPDmzNnDgsXLmTbtm3k5eVx9uzZCve02pKfoz/YgyIjfdJeoHC0MA8AIdd+Kv/LL78kOzub22+/3Vnmbbyec8mx6gbf9SUgnMtZ+1RaZD2NZ1DzQTy38TlGfzeabbduC3gDXI8ElmPHjvHwww+zcuVKtwOcucPf//5353KnTp3o3LkzLVu2ZPXq1QwYMKBC/cmTJzNhwgTnem5urkdh7WuDVQssgaUmb4pzKS7K5tcfH0PVCvW3eexv8IhqloV9WdOXca2jK0fEOR/sUy6VlNsRQLENwsO7+TT4XWUPn/79+7NmzRqXevfee2+NrrM1YTIpRMVEEFRwlqLYWHp8M7/mnXxEzufz7cah/mHHx7vZtC6tUm1KdFj9OxYWl0oEm7w3Xq7KDq+wsJAhQ4YwZMgQJk+e7HU/Lm3n5GIGQqMuLIGlgaJ7v3WLqv1LwNy5cxk6dCiJiYnOMm/j9ZxLnlUXEKIDxJspt1QXoKIt9TOe6OBopvWdxpPrn+S+Vffx+pWvExYgBsmV4dHdZ+vWrWRmZtKtWzdnmaqqrF27lrfeeosVK1b4JOHaRRddRMOGDdm/f3+lAktQUFCdG+WqApQAElg8c7SGo3u+xxryA5pNxlYUrO/rTEbkWC637vhIDssdybWedE7dCm3JlbRZRliDw0TlpNPhYt+47VX18AG4++67efbZZ53roaE+vDlkZqJWMXXpN4Sma2b8xLFdWSgy9O6ZQFhMMGFxIYQnhBIeH4YpJAAEFpuZuDDvBJaq7PAAHnnkEUCfpvY1Rbm6wBIeHeXztuuTPLvmokEtH7xHjhxh1apVfPHFF84yb+P1VEa+VTdyjQmqfxuixYd/ZcHuhYDnv5uvPHQBrm15LY3DGnP/qvv5cNeH/KvLv2p1PHWBR3efAQMGsGPHDpeysWPH0rZtWx5//HGaNWvmk4Rrx48f58yZMzRuXDHldn1hEwJTIMkrHiLsGpmOKR/QuHktk9/5kBXft6NN69Y0ad7C67aqe/iALqD4y6DbnJWF1s1z4z+vEEKfQvIThUU2YhoE021MxYy/gUAJQQSHlnrVRlV2eP6mOCeHSGD32bOcRRBqMRNqCSLUYiEs2EKQyVxl/JZAJs8pCNTu7XzevHk0atSI4cOHO8u8iddTFQX2cdZWsPIlU9c9hKQVoimxtIxwP6SFrz10AS6Jv4SY/2fvvOOkqO///5y6/SpHOTqCoChgRbFEo6JosH4lKjFGjRpjxWiEqLHEgolREjVYfgbxqwRLRI0NUSKgggqCigrIAdIPrt+2qZ/fH3N3cHBl7273il+ej8fc7s7Mznx2b2fmPe/yevvzqDAqWvox2pUWGSyRSISDDjqo3rxQKER+fn7d/FQarg0bNowHHniAc845h2g0yt133815551Hz549KSoq4ve//z2DBw/m1FNPTcNHTA8uotXS/J0Bw97gPVpbO3YgdUh1eSxtpbmLzwsvvMDzzz9Pz549GT9+PHfccUdavCy2ZeGvrsbOcDhyT4RwEcgIITKSA5QwHLK7d/wJvTFsSccXab2Htak8vEwTW1+EK0lcWu5CRfneKwgX1XFQXAdD98LummWiuA6K66IKF9V1UISL5goUXDQhUBFoCDTY9SiBJkk1jzKaLHmvFRlNltFlGVWW0KSax5rnmixTsWkD2Xl55OR1Q1dkNFnBpyroioKmKKgqJLUEPl1HQrCpehPQOkPAdV1mzJjBJZdcgqruuiQNGzas1Xo9jRGzYgg0NKVjPYWW6yC5cU4adhP3H34JwRRvQDJRoQvw8pqX2Rrbyin9T2lyvY4m7f+1VBqurV69msrKSsCzlr/66itmzpxJRUUFhYWFjB07lj/96U+dToulU5krLYsIIeOdSBS5c0hne8Uvbe+r09zF56KLLqJ///4UFhby1Vdfceutt7J69ep6rufWUrF+PYrroqfBS9QShOtirq1k45RFRIGE7E2WIuHWTEKRQZOQVAVJk5F1BVX3HmVdQdFlFF1B1VUUn4KmK+h+Bd2vkrAF4ZzOdezVYlTHcRWdQE7rylIzlYeXKr3LS7AVmf/t4Sdh2SRsi4TtkLBtkrZN0nVJCoek61IRrSRLVQjIMpYQGMLFFAJTCCwhMAFLCCzAFGAjYUgQQ8ZCwkbGrnuUcYWEI8k4royLjCNkXKHgKgo4eFMtBUO8x0rB3gshWDmHUOUex5AcRm+FIfD++++zceNGLrvssnrzNU1rtV5PY8TtBJLsI24Y+LX63qyKaAxFU/GpGposZbQgoMKs9fSEUjZWIDMVuluiW/jr0r9y3pDzOKxHepV1002bDZY947ypNFwTu2lRBAKBeo26OitdWD4DAFX29A40rfVJceml7SeDVC4+V155Zd3zgw8+mF69enHSSSdRVFTEfvvt16b9V6xZA0D2kNZVLLQWC5c1eTrhEXmIpA1JB8lwkCwXyXHRbYFkOSgxgeKaaAI0F3S8ydfEd+8IgSWgquNTVRoktrkEgGB+60IPzeXhGYbRYGViunCrq0BROGX4ARnbR0sQrovj2Ji2g+U4GI6DZdtUlJXiKiqoWs28mmWOg+k4TP9mOaWBLC457n5cAbaAg3Ka1yJqiLFjx9a7JuxOa/V6GsP84TtCsQTTf+mlLNiKiqOo+MxdVWcCCVtRcBQVV1FxVBVX1XAVFVHzXNS8LzvgR1Y1ZE1DVjUUzXuuahqK5uk3qbqGqukoqoaua4hEjB0lP4AEO7Z8wUI5yxO3VHV01Y+u6vh0P5FgNnlZu0JFmajQBZi9ajZ+1c/Nh9/chm+2feikp6XOR1u6NXcFmpPIrkUIwemnn867777LnDlzOPvss1u5x7aHhFpz8Rk9ejTgdbBtq8ESW78eHcgdOrRN22kpknBRekc4+aKDml95D2zHxbAcjISNkbQxkzamYWMlHaykzebN1fD+NpZ9U8Lyuz5G0iQUVUbRZBRNQdNl9BqPTDCkcupJA4mE208TJbatDIBQ95xWvb+5PLxMGisAbjSG0DpP6agky6iyjrrHkHo3k0g++1MQtspv9v9JBkeXfrKcGJrQyJt4JZZlYZkmtmXhWCZmIk6v/gOxLRPHtLxH28IxTRzLwrUtXMubypJJVNsmFo0iOTaSbSHZNrJjI9sWSs1zxbHRnL37h5mqC2NhYelbLCx9q8GxSgKeOuJvHDX8pxn1DFab1fQK9eoS3Zv3GSwp4nayzj0tvdSLmioeqRHvY3MS2bVMmzYtfa7SNoaEWnPxWbFiBUBaErqTGzeCz4ee3b4VH7IQ0MoqIVWRURWZkL/hi+agqMlXX5UgEg5ulYnkgnAErqCmbb13Ny0jUQ28kXSY+PP2S86N7fBCyaFerRP+SiUPb/v27Wzfvp21a9cC8PXXXxOJROjXrx95eW3zUIpYDOHrmqJ3u5MUBr42iMR1FJZjEJB1Lj3zzHbZnxACw3UxLYuEYWKYBrHKSipLd/KTLA3FrcayTUzbE6g0HRPLMVmz7VteSLxNVcL7vWeyQjdqdQ59pVTYZ7CkSEv6qLQXUms8FI28pTmJbPAu9n/9619ZunRpmy/4ngHVNg9LcxefoqIiZs2axemnn05+fj5fffUVkyZN4vjjj2+w/LmlONu2YaZJUKwlKMKtaeqXfiJhnSn3HNfkOkIIin6oZO7ULwiF2tdbEC+NAgHCfQqaXbe1PPHEE/WEKY8//njAS2LcXdSsNYh4HOFPf37QwoUL+ctf/sKyZcvYtm3bXt7PdDeYTQqTLLnz6nU0RtxNtquhJUkSfkXBryhk1XpGCgqgGeG7Bcvf5oWv3ibs9xSRM1GhWxwrZsY3M5i7YS69Qp2nIrcpul79XAfRwhzXjNNiD0uNCFhjHpbdqZXIvuyyy+q8KfF4nIsuuojHH3+8zSXCO3euRdfjuO7ertJ0ous677//PmPHjmXYsGH87ne/47zzzuM///lPWrYv7dyJW9D8hXPAgAFIkrTXdM011wCe3sQ555xDQUEBWVlZTJgwgeLi4ga3VVueToZDF00hSRIlZV7SYLdu7VtNlKhMojhJtHD69vvhhx/WaVkA3HXXXYiatgi7T201VgCkZBIC6e9jE4vFGDlyZJO5g6eddhrbtm2rm/71r3+1en+GMAl0kgT+lpB0DfxS6gbL9OnTGTFiBFlZWWRlZXH00Ufzzjvv1C2/6qqr2G+//QgEAhQUFHDWWWexalXTzVlTIWZ4ysHhgOe9rb05233a/eYsOzu7rkL3v//9L8uWLePSSy/dq0J3d25acBMvfPcC+2XvxzOnPtPmMbcH+zwsKdLZegnVGiyVpRtYvOhsUExPz43dFGhrHiUJFJ9nHKSikNqQRPakSZMYM2YMZ511VpvHHottA6B79/S3r9s9Cbxv3757qdymE628HIbs3+x6TYXbYrEYY8eOZeTIkcyfPx/w1D3Hjx/PkiVL9tbkqN1OB2t17NzpaWT07Nm+d9mJahO97cVlHYaUNJF6pN9gGTduHOPGjWtynXQ2mE1IJmV2Bd+uX45f9+PXg/j0AP6aSengsuHGMIRJrpy6ynCfPn2YOnUqQ4YMQQjBzJkzOeuss1i+fDnDhw/nsMMOY+LEifTr14+ysjLuuusuxo4dy/r169uUD/WPFf+AAOSGUxelTKVCd3cuPvBibllwC+sq1/Hsyme57ajbkFO5o+1AOuevqhPS2TwstZTv+A4tqxq3cjia1g1JyNSqz0q1irOShJSQ0AM55HdvvmxtT4nsN954g/nz57N8+fK0jFmu6Vfh83WOBmStwUwm8UdjuClosDQVbps3bx4bNmxg+fLlZNX0l5k5cya5ubnMnz+/Xpki7KqwS1ce0QMPPMCrr77KqlWrCAQCjBkzhgcffJChuyUSN6SgecLJ1yMDhT3a2WCJO/iktonGdSSyaSJCHZPc2JIGs82hIPO9v5ifL/xlg8slFxQhobgyF3U7k5vOvqfB9dobAxN/C/odjR8/vt7r++67j+nTp7NkyRKGDx9erwpxwIAB3HvvvYwcOZINGza0Kak/KVn0jIbp22NQo+u0pkJ3d04bcBrH9z6eGd/M4Ikvn+DMwWcysmBkq8fcHuwzWFIk4Tg8un49W0uXIEsgIdWaBUiSZyLUVhHVPt+1rGZdyUtWrBWs35RM4gqvT48Q7OrYIwRuTYaK99yb74pdnXne0LyD4fJtO/kDENnvfzhqRMMnj5bQkET2/PnzKSoq2qtk7rzzzuO4445rsYR5rcHiOF33wlO+bh2K6xIYOKBF79uzI61hGEiSVE9zyO/3I8syH3300V4GS12jAzk9BsuCBQu45pprOOKII7Btmz/84Q+MHTuWb7/9llDIM0YaUtCc9tcbuOnkh5l+00IcGVxZwlVAKBKoMpIqIakysiaj6DKapqD6ZDSfgu5T8fkVfD6VQEAlENDIsarona2jRwJoWUF82WEU3975MYYBPtXZa35XQbEsRKT9DZaWNphtjhnjn2P1lpWYVhLTNjFsA7NmMhwD0zaxHZN/l89lfeWG9H+gVmJINkG1dR4ux3F4+eWXicViDeaFxGIxZsyYwcCBA9vc2y4pWRwRbnkVYEsJakH6RfoBdIk8ln0GS4ocxLcUOQOYVZ6DQMKtMTs8A6L+89oJvOTSmnviXe/Zw+1WQBmS5L1Lrllb3m0rMqJmOXXrFEolbBXd8GEAYOlGWj5nQxLZkydP5te//nW99Q4++GAeeeSRve5AUkFVvRhypnNYMknlmu8ByGph19g9w21HHXUUoVCIW2+9lfvvvx8hBJMnT8ZxHLZt27b3BmpCQVIrpcn3ZM+ky2effZbu3buzbNkyjj/++CYVNDdfsIW+vYdjGQ626eBYLm7NJGyBSNpehZEjcF2vvYXtgoVELIWxSa6N4loowkLBRsEhLufRO1CWls/e3gghUBwHEWn/xoctbTDbHAMLhzKwsPly/jeenEeokzQaBDBlm4DSMoPl66+/5uijjyaZTBIOh5kzZw4HHrirMu4f//gHv//974nFYgwdOpR58+ah621L7DVki3A7dXD+cueX9An3oXsw9fYAHcU+gyVFbpP/zMCB19K/3xWt3obnzhe7vXYBuU29QzZt/oo1a+CAbkNavY1aGpPI7tmzZ4Ox7379+jFw4MAW70dWtJr9tb3jbkcR37AeH5AzpGXf+57htoKCAl5++WWuvvpq/v73vyPLMhdeeCGHHnpow7+LWoOlrR+gEWoVqGvLd5tS0OyeX8Jvrz60we00hW27ROMW0ZhJNGaxZuEyekz/K87Ey8nt3g07YWLGLWzDwkp6xpBlOtimwLbLOOjUtv/WOwKrqgpZgNrOZfAN0VyD2XRhyg6BTmSwWLJDsIXdiIcOHcqKFSuorKzklVde4ZJLLmHBggV1RsvEiRM55ZRT2LZtGw899BATJkzg448/brVeihACU3GI6JFWvb+lHNr9UF5c/SJf7/yagwta3gG7PdlnsKSIEA6S1Lavy8s7kHZ73fYEJ0ny3LmuaPvFvzGJ7HTj2J43qCsbLMbmzeD3o0VSP6k0FG4DT+mzqKiIkpISVFUlJyeHnj17MmhQA/Hr2rBjGtoa7Inrutx4440cc8wx9TRJWqug2RiqKpOT5SMnywuDlSyqJKv6B3LPOYae/Qvb9Bk6M8mdOwDQcjs+d6s9GswKIbAUl1ALDYRMUTeeFnoudF1ncI0n9bDDDuPzzz/nb3/7G08++SQA2dnZZGdnM2TIEI466ihyc3OZM2cOF154YavGmTDjuDJEfO3jiTt1wKk8s/IZ/vz5n/nf0/+3XfbZWvYZLCkihIXcgnK49kKiJs6fhgtYUxLZe5Lqeg2xfMVv8Pm6tsHibN2G2cILT0Phtt3p1q0b4OUM7dixgzMbELeSJC+0KGWgV8Q111zDypUr+eijj9K+7aZI7NiJg0Rer8xpq3QGkiWlAOgZMFii0Wid0B3A+vXrWbFiBXl5eeTl5XVIg9mkEcdVvDyJzkDSjCMkCLYx1OK6LobRcAi+tgS+seWpUFrpGbZZgZxWb6MlKLLCWfudxV+W/oW4Fe80/6+G2GewpIAQLkLYdcminYlaL40QXScfRNO8fjADBnTuzqBNIZfsRNQYGKnQWLgNduWEFBQUsHjxYm644QYmTZpUr1KnFiEEQkq/wXLttdfy5ptvsnDhQvr06VM3v2fPnq1W0EwVq6SEan8YXe98x1c6MUp3AqC3US23IZYuXcqJJ55Y9/qmm24C4JJLLmH69Okd0mC2Ol6jStxOuRi1fP7dQq5fdCOOJJCRkEVNAQQS+FpmsEyZMoVx48bRr18/qqurmTVrFh9++CFz585l3bp1vPjii4wdO5aCggI2b97M1KlTCQQCrW7OCFAR9QzbnGD7eOKqzWr+/f2/ObzH4Z3aWIF9BktKiJpwiyR1vhOqJNeGhLqGwWLbBpIk8Pt+TW5un+bf0EnRyitwhw5Lef2mwm2rV69mypQplJWVMWDAAG677TYmTZrU6LYE6QsJCSG47rrrmDNnDh9++OFeOUmHHXZYixU0W4pTWkY02P6JqO2NWV4OgC8/dUM3VU444YQmvZ4d0WC2Mu593rCvfXIxavlm0wqiPotxytEokowjXBzh4LoOiqwy7rDzUt7Wjh07+OUvf8m2bdvIzs5mxIgRzJ07l1NOOYWtW7eyaNEipk2bRnl5OT169OD444/nk08+oXv31iewltcYLNntZLBMWzaN7bHtPHLCI+2yv7awz2BJgdrQRaf0sNSEhIToGqWe5eXrkSRBONyvo4fSaqxkEn80it03dYOrqXDb1KlTmTp1akrbkSQJIUl1ysVt5ZprrmHWrFm8/vrrRCKRuryU7OxsAoFAPQXNvLw8srKyuO6665pU0Gwp/pjDAH9Pyl78EDmgIQV8KGE/ctCPHA6gRIIo4SBSQE9fH6sOwK4xWPxtuJh1JSpiXjVXVqB9c3ZqVWL/NOHv+PS2qfE+80zjCrCFhYW8/fbbbdp+Q1TWfG8tEY1rLa5web3odX598K8ZlNO45ktnYZ/BkgJ1HpbOaLDUeFi6Skho3dY1AMyd+zmz52zAkRSErIKiIStei3ZV09F1HV334fPpBPx+gkE/4YCfcDBAdjhAVihITiRAXiSErrXvz7iiaB2yEAT7D2jX/dbiIuE66TFQp0+fDnh36Luze9+clipotpRePY4lMLAX8eXgqRElaqb6COGCa4FrexMOks8LrwhrG5IiQBFIiuTpwOgykqYg+RRkn4YcUJH8GnLQjxIJEBozHLkdfztmRQUq4G9BKLErU3vhzQ61r8ESN2NILm02VjqKykQFALmRzP9OKo1KDMdgQNaAjO8rHewzWFKgzsPSCZNu5ZowlegimiYVpnfS1nofS66lYBgmlmnUtXK3jQR2vArDtZGFjSIcNKnp8IcjJBxJwZVUhKyA6uPin5/HQYN6Z+QzVK6t0WAZ0jINlnQhJBnHSV9IqDlaqqDZUhKRnmzNkfnJ+YNwY0ncaAInlsRNmIiEgZswcZMWwrARSRvXshGWizBdhF2bD2IhLBlMGVcoICkgad5NhqwhKbvfbFiARfK7uRT8puEE6EzgVFaCLKNone88kgmq4hUA5ITSn7PTFHErjuZ2bon5pqiqNViyMpeELoRgybYlPLbiMTRZ46BumRepSwf7DJYUqDVYOqOHpTZM1VU8LB9+9w3H9PRxw4XnIaeo1mpaNhWxBOXVcSqrE1TFE1THEsQSCeIJg3gyiWEYGIZJrLKUQHwHr839kI0jDyY7HCA3K0xeVjBt3pjYunXoQO7+zfcRygSuLJNYs4b3/vlvZElClmVkCe9RkdBUjRHjjiMQ7hzlpM0RcmFndgDfgMyV2LpJE6c6jlMVxymtpPzVEowNNiUz5iL7NaSAjhz0oQR9SEE/SjiAHPIjhwKeF0ZXkVSlTSEpNxoFreOaVrY3VQkv6TY3q309Sp7B0nW/52qjCsWRCPgzc/yuLlvN3Yvv5uuSrzkg7wCePOVJ+kS6Rj7hPoMlBYTwJOTlTph0S82YukrSbWGkmrJkbsrGCoCuqXTPidA9p/nkvXVbdzLjyX9A8fd89t73ey23hIItqbg1YShJ0VE0HVXX0X0+/H4/Qb+fUChAJBgkOxIkNxwiLztEt+wI4YCOsWkzbiCAHu6YnjDVkTyGL/8Qln/Y6DqfrPktJ912XbuNqbW4tkuWkFAjmfU6yH4d2a+jFeTg9sqj9F/fI/vySK6uXUMAyZqpssFtCNepCUk5nkdTOF5oSjh1sgKiptloHdKuxzwxBmf00Xx9xxyG3HwK/uyO+f20F9XJKgCy29nDYrgGmui6BkulUYnuZGb81WY1V827irxAHtNPns4xhcd0qbywfQZLCrQ26XbhwoX85S9/YdmyZWzbto05c+Zw9tln1y2PRqNMnjyZ1157jdLSUgYOHMj111/Pb37zm5T3IdeK2XURgyXXV47tpM/V2dB3/Ic//IGdFdWUV8cpq6rmib8/zNw35hCPVTNgyAGMv/AyIjl5WKaBY5k4RgwnXoHpWiSFTVQ4jXbmdoTE6O++Y79Egrk/OR07EMIJhhChMFIoTL9zzmDUqcel7fM1xHEfvIlVWYXtuLhC4NQ9ChxXsPF/zsOqqs7oGNJFVYWXq+LPzlx57Z7IQT/9HjkT13Zwo3HcaAK3Ou6FoWIGbjyJGzdxkwbCsBCOAAeE64JdE0ZzQLgSOBpCaDWNwLxmX0KImsZfov5zw0Eij1wrQmzLzh+dwVL+5TwcI46s+ZA1P9Xl61EcierKdahaEE31o2khVNWPlMFuzknHQHe77qXtP+YiyJD9/sSXTxC348w+aTY9Q+mRJWhPuu5/tR1pbVlzLBZj5MiRXHbZZZx77rl7Lb/pppuYP38+zz//PAMGDOC9997jt7/9LYWFhQ2KhjWELHuJZY6bnl5CmUalBEMakLbtNfQdB3wa/Xrk0a9HHg8++CBzX3uZmTNnMnDgQO644w7mPDONb7/9tlHpbNd1qYomKKmKUlYVo6I6RmU0QTQWJxZPsMWfh9NrLZJpIUWjqLEoasl2+u74AT58HVZ9l7bP1xBKIIASaLwfSpGi47RBuKo9qdiZQAWCOe1nsNQiqwpyTgRS8Nyli6LXPoYlLv68H1cZd8XX/yV3zv/Um9c7K4KTn8sJb0/Ya31VCDQhvHw2AWpNvaOG5D2XJDRkVElGQ0aTZFRJQZNkNElBlVU0SUGTVe+5rKLJGqqsscFejy667qWtl5vHNjn9/bKEEPyn6D9cMOyCLmmswD6DJSV2eVhaZvaOGzeOcePGNbr8k08+4ZJLLqmr0Ljyyit58skn+eyzz1I2WFTVu0tznb2rKlLBU2Z0AAfXtTFtG8M2sWwL07boltUDfxqz7QNKCQk5PeWw0PR3LIRg2rRp3H777Zx11lkAPPfcc/To0YPXXnutXkO43ZFlmZysEDlZLYshfzfsAKoKOl5a3lZVXLNrqAhXlcXJA7LzO7dgVbqwo0l86Phzf1wGi1nyAwBbj7gHPb8fwkpyghGjt7wTNejDcgwsx+vgbDkmlmtiOxaWa2K5FrZjY7kWlmtjCRvbtWueO9jCxnIdLOEScy0sDCzbxRYuFi4WAksIbMBCEJMlRpZ33e+3mx3B76Q/aXhz9WbKjXIO7d7y/l+dhX0GSwq4GRKOGzNmDG+88QaXXXYZhYWFfPjhh6xZs4ZHHkldwEdRPIPl67Wz+WLNa7UjRkKgKwaqZGE4fmTJRZIcFMlFllxkHGTJRZGbrjZZDWw2TkKWQyhKCE0No2phfFoEv+5NIX82IX8W4UAWWcFsAnqwwbio65qEtSrico+UP19bWL9+Pdu3b6/XuC87O5vRo0ezePHiRg2W1lLtC7Hz6Mw1kksVR9FwTbOjh5ES8QqDPCAnv2UddLsqTszEFhKKrxPmw7UBtybBNnvkaYT67FJo7t8BY3n1ujNRlK5bJRR3EwTk9Jdkv7j6RTRZ4/Aeh6d92+3FPoMlBYRbk3Sb5iqhRx99lCuvvJI+ffqgqiqyLPP0009z/PHHp7yNdSXV/GvVOQzIqWBAt1yQakSoJRkoJy51Q1IVQPEaJUqK53StfS4pSJLqTSjIioIqa6iKyrYd81GczShsQHUTaG4C3U3id5LIhkCwSzGjZLcxOa6M4fgx3AC268cmiEsACZXCAGha+7gja0XQevSobyC1pXFfY5hJg4gRo6Kg4zU2XFUFq2sYLMkqEwtBz9zMGixTp05lypQp3HDDDUybNq3eMiEEp59+Ou++++5eeWbpxklY2KLrXkwbQ9QYLGok82JnzWHbLj5/1y0dTwiDPDV9Hb1d4fK3L/7GzG9nMumwSYT1rps7tc9gSQE3Q8Jxjz76KEuWLOGNN96gf//+LFy4kGuuuYbCwsJ6XoGmWLN1Pe9vPJFbBsX4xdi9Y8Vt438anOs4LlEjSlW8kmiikniymphRSdKoxrCqMa0oll2NbcdwnRjCjYGIYdsxvikdSqRHx+iXZJKSzcUAhHt2vIqpq6hgdY2QkB01qZJAzeAd8eeff86TTz7JiBEjGlw+bdq0dquUEEkHR+oa/5uW4MQrcAXo4Y7vRG1aDps2Jun+9xuRZRVJ8W7EZEVBUlTkusl7venbzTiiB7KqI8kKsiwj1UyyoiDLCpIseevL3u+01yEHEundHS3gRw/4UXUfspKeyp6kZBJS01fS/Kclf+Lfa/7NzYffzC8P/GXattsR7DNYUkDUCcelz2BJJBL84Q9/YM6cOXXde0eMGMGKFSt46KGHUjZYzj7yZG589S3MdlTmVxSZ7GAW2cEsoG/K73tm0Toefus7Zl7aPnketc35iouL6dVrl8ZHcXExo0aNSuu+SjdtRwWyCtsn3NUUrqKC3TWqxtyYRSyDFajRaJSJEyfy9NNPc++99+61fMWKFfz1r39l6dKl9X4jmWLt5qVs2PkVb/78CSRJqn9hrJtUFNWbPOVnDbnmtaKoKDWvVU1DUXUUTfVK8zUNVddRa8r0VZ8f3e9H0X3oPj+az4fm96P5/ej+AJo/gKppdRfhtiCSVZiuij9NF+220KNHLmWxcj5dsgpXSAgh4QpwaepzriKk53g5fbg1uX2uNyHqPbq4MH/vLUjIqLKGoqioso6iaKiKhqJqqKpeM2mous/7H9X+n3Qfms+H6vOh+n3EZZMN7nY+/uK/+PUAfp8fn+bH7wvUm2qbqG7auoEv1y5D2WET7plLMDuC3xck4AvwUfliXlnzCnePuZtzh+xd+NHV2GewpECth6WlSbdNYVkWlmUh73GyUBQF122ZimlATWDanb+X0PrSGAAHFLZPQtzAgQPp2bMnH3zwQZ2BUlVVxaeffsrVV1+d1n1Vbt1OPpDXtxNk3ysKIk3S/ZlGTjgk1cx5V6655hrOOOMMTj755L0Mlng8zkUXXcTjjz+ets7TzWGIOFmBbuQfMAArmcQyTWzTwDY9pWfXtnFtG9swcB0H13URruOVRwuBJ0yc3k7ddUiSJxsjyUiy5BlUUo1BtZthpcqCntpORvVxPK+CrBCs/B6bzpGXc8p9s2isD7ywbVzbwLXMukfx90OQhk0i8IvbU9p+2XebqNiwGTuZxDIMrKSBbXj/Q6vm0bZqHm1zl4q3aWLEYziOhe1YOG7to40jLJwaaQr9eIkN+nZ+8/X1TY5DFQq60IjLyV0zi/Ze78J+P/9RGCuwz2BJiVoPS0uTbqPRKGvXrq17vX79elasWEFeXh79+vXjJz/5CbfccguBQID+/fuzYMECnnvuOR5++OEW7UeTHQy75VLtd911F3fffXe9eUOHDmXVqlX15qUrxr+l3Ktk6h5JX0JZc9/xjTfeyL333suQIUPqypoLCwvTnqcQK95JPtCtT8cbLBFXpluFyXv3foyjSDiajKtKoMlIqoKsy8iajKIryLqC5lNQfSqaT0H3q3WTP6DiD2gIVSI7oKNnwLDQTAfDlxmDZfbs2XzxxRd8/vnnDS6fNGkSY8aMqasgaw8sy6Rbt36cfeudbd6WcF0cx8E2DSwjiZWseTSS2MkkpmFgG8mai2jNBbXWOLIsb55l4ViWp0dk2zi2hWPZuI6NY9ue0eTYnmaN43U8riqvpAqdw7NWocmOpz+DS2n2KFLNjnjggQd49dVXWbVqFYFAgDFjxvDggw8ydOiuhN2ioiJuvvlmPvroIwzD4LTTTuPRRx/dKyetJUi1nqoaFVnhuqA4GL7U8zryDuhL3gGpe5ZTxbUc7KTBpdEq4hgYRoKkmSRpJDCsJEkriWEZJK0kpp0kaRsYtoHhJBlROIr98gaTxMAQBolknNIF6xGlJqdcdHHax9pR7DNYUqC1wnFLly7lxBNPrHt90003AXDJJZfw7LPPMnv2bKZMmcLEiRMpKyujf//+3HfffS0SjgPPYDFbYbAADB8+nPfff7/uda2bcXfSFeMvrjLQlPTmCjT3Hf/+978nFotx5ZVXUlFRwbHHHsu7777bqAZLazF2lBDTAujBjq92yVdUVNMlCgRNFy3hoLmgugJNgC5AA2Sa/1/U3rttQmAAJgJTAluWsCWwFQlHlnAUCaFICFUGVULSvMaDsq7UGEcyiq6i+pR6hlHQcIlnpf/OfNOmTdxwww3Mmzevwf/1G2+8wfz581m+fHna990UlpXAF0hPCbcky6iyjKpp+EPtl0j51Yy7mffuZ/S8cyXSbpIHLUm3XbBgAddccw1HHHEEtm3zhz/8gbFjx/Ltt98SCoWIxWKMHTuWkSNHMn++F3+54447GD9+PEuWLNnLM91qkgkkSYDe8WX1sqaga0HyIkHSoQ1c/IEPbXgEpZ2bw2aSH88nySCuMAHZq6xpASeccEKTzeV69uzJjBkz2jg6UBW31QaLqqpNusPTGeMvixkE9fT+5Jr7jiVJ4p577uGee+5J6373xC4tJRbsHNoPuqaCLnHi7cc0uo4QAmyBZdgkEzbJhIWRsDGTFqbhYCVtrJrHlT9UkKXKdA/quKaDsATCdpEsF2wXxXHRbIFiusgJB3UP40gH9EaMowgSkpv+hNdly5axY8cODj10l+aE4zgsXLiQxx57jKuvvpqioiJycnLqve+8887juOOO48MPP0z7mABMK4ke6Bo9nhrDiFbjU5x6xkpLeffdd+u9fvbZZ+nevTvLli3j+OOP5+OPP2bDhg0sX76crCzvuJo5cya5ubnMnz8/5Ry/5hDxaiRAylDfno5E2ALZ/+O6xP+4Pk2GEK6V9pLmdKLJLmYru/d+//33FBYW4vf7Ofroo3nggQfo168fkP4Yf3XSJj/c/oqm7YG+rZS8aDnLjhsPkgSShJBkkGSQJe9RkkCumbfbfEmueS7LXjm6XDNJEqhelQOKCorXgA9ZqZsvqSpCSPhciZy8IKgq9o5i1B5N/78kSQJNQtd09LBOU6ZWOmT+HNslmbRIJmwSCRsjbmHGTcIvFdGvZ/qVZk866SS+/vrrevMuvfRShg0bxq233kq3bt246qqr6i0/+OCDeeSRRxg/fnzax1OL5Rj4gh1/N98WkrEofjW9eTSVlV5ZdF6e51swDANJkvD5dp0v/H4/sizz0Ucfpc1gcRNRZEDyZcZg2bJlC7feeivvvPMO8XicwYMHM2PGDA4/PPNaKFphCPOHqozvpz3ZZ7CkgCustIvGpRNNcTGdlp9ARo8ezbPPPsvQoUPZtm0bd999N8cddxwrV64kEomkPcaftF26hbuuPkJTbMk/An9MIre734vpuy6ScMF1a/rJuF6DPFfUPHreCVyv8sBbvvtzb5kkHITrINVMQrh1z6Xa58LBEQ7lwq1rwqeNPKyDv5H6KKpMKOwjtJvBapUkKAb0DMjyRyIRDjrooHrzQqEQ+fn5dfMbMsL79evHwIED0z4eANdxsFwDX6hr380b8TjpdJS6rsuNN97IMcccU/e/OeqoowiFQtx6663cf//9CCGYPHkyjuOwbdu2tO1bxGt6bvnTH1IrLy/nmGOO4cQTT+Sdd96hoKCA77//ntzczJd+u3ELp8KADCa0dwT7DJYU8DwsnfdCq8kupt1yg2V3SfsRI0YwevRo+vfvz0svvURBQUFaY/yO4+K4gh5Z6Vdw7AxURvbnq58dyk2/O7Ld9/2fPy4mEbeY8NDxXoWZaYHeeQ3sWuwSLwlbyf1xet32xKiKAuDroC7f6cJIJPHr6Stfvuaaa1i5ciUfffRR3byCggJefvllrr76av7+978jyzIXXnghhx56aPryVwASXuWiFEj//+TBBx+kb9++9cL+mTKGaxFCEP14K1Xv/QAI8i46IKP7a2/2GSwp4AorrRos6UZTBFYaqlhzcnLYf//9Wbt2LV9//XVaY/w/lMcB6JvXtd3hDWEaDkFb4OvWMcZYPGYRCnm/T1mWwd81DACnzDNY1Lz2+d6a+802lQuVDhLl3t28P9LFPSyGic+XnkvHtddey5tvvsnChQvp06dPvWVjx46lqKiIkpISVFUlJyeHnj17MmjQoLTsG0AkPYOFDOSwvPHGG5x66qmcf/75LFiwgN69e/Pb3/6WK664Iu37qqVq3g9Uz99E6OheZJ3UD+VH5tH+cfmLMoRwzbSr3KYTXRFpEY6LRqMUFRXRq1cvJk+ezFdffcWKFSvqJoBHHnmkVYnCq7d5J+tB3br2yboh1q6vQEKisE/7df3dnbjhEMpApU2mscu9jtJqtx+fEdsQRoV3DPiyO+Z3ki6ShoWvjUaxEIJrr72WOXPmMH/+/CY9D926dSMnJ4f58+ezY8eOlBvDpkTS83pJwfT/T9atW8f06dMZMmQIc+fO5eqrr+b6669n5syZad9XLclvywgclE/uWYN/dMYK7DNYUsIVnTvpVldolYfl5ptvZsGCBWzYsIFPPvmEc845B0VRuPDCC+nZsycHHXRQvQlaH+Nfu8M7Mezfo2ufrBti/YYKAPYbmNPu+3Zdl6QtCOW03EvxwAMPcMQRRxCJROjevTtnn302q1evbnBdIQTjxo1DkiRee+21No7aw6n0DJb/KyGhZGWNhyVNBstdd91VI+62axo2bFjd8qKiIs455xwKCgrIyspiwoQJFBcXt3m/puniD7TNK3bNNdfw/PPPM2vWLCKRCNu3b2f79u0kEru6zs+YMYMlS5ZQVFTE888/z/nnn8+kSZPqabW0GaMmJBRMf0jIdV0OPfRQ7r//fg455BCuvPJKrrjiCp544om076sWfUAW5sZqRCurRjs7+0JCKeC6FpLUeb8qXRVUJFpue27evJkLL7yQ0tJSCgoKOPbYY1myZAkFBQVpH+PGsjgg6J+jIoRot94t7cG2LVFsBPv1zXxZ8+O/8TQpQqqELIEjwAXCrQhHNaeFsTuZ6LfjVJsggdyFO+u2hGS1Z7T7c9L3O2lMRymTOiaGJfC1UW9o+vTpgCdLsDszZszgV7/6FQCrV69mypQplJWVMWDAAG677TYmTZrUpv3uiaj1sITSfyPVq1cvDjzwwHrzDjjgAP7973+nfV+1hI7oSWzJNowfqvDvl5Ox/XQUbboKN9QBNZlM8rvf/Y7Zs2djGAannnoq//jHP5pUJxRCcOedd/L0009TUVHBMcccU+dK6wwI1+zUSbeqLNGKIiFmz57dovXbEuM3t39Hd2zyH+mDKVRiUoAEAZJyAEMOYqohbCWErYUQWhhXDyH5spD8YRR/FmogghbMwhfKwR/KIhDKIZiVTSCYlZZeKG2hYkcCU5fQtMz3UfFrMknLZcCgbBxXIByBqkoM/Emf5t+8B81pYdSSqX47bsz60VUxNEVt0q0/J30Xx8Z0lDKpY5J0ZHxtFKpL5VwydepUpk6d2qb9NIsZRwgVSU//+f2YY47Zy2O5Zs0a+vfvn/Z91SHX3FTs87DUp7EOqJMmTeKtt97i5ZdfJjs7m2uvvZZzzz2Xjz/+uNFt/fnPf+bvf/87M2fOrJNPP/XUU/n222/TrkjaGryy5s5rsMiyjOtmNmGwrfjdGAWKxdJD7sdNRhFGNRjVSGYU2YqiWDF0p5qIuR2fE8fnJgiSICTiKFLjn80VElHJT4IACTmIIQexlCCWGsJWw7haCKGHwRdG8kVQAhHUQDZ6MAs9mEUwnEsgkk0okoMWyPL0TlqIWWEgQu3jgQv7FZKWy7HXj0RV02sg7amFAZntt+MmbGTt/5DBEo0iIaGF06eG3JiOUqZ0TBzTwHZlfKHOIZLYZowYAj+ynH6Pb60sxP3338+ECRP47LPPeOqpp3jqqafSvq9aYku2Ioc1fD9C7wq00mBprANqZWUlzzzzDLNmzeKnP/0p4Ln4DjjgAJYsWcJRR+0tQSWEYNq0adx+++11eh/PPfccPXr04LXXXuOCCy5ozRDTitvJPSyyJCEy1RAtTZTEHHoFXQ4/65oWvU+4LslkjFh1BcloJYloJWasEjNRhZ2owklU4xrVkNxl/MhWDM2OEjQ343Pi+EWcgEgQEgl8ktXk/pLoxKUASckzfkzFmyw1hKOFcLUwrhZG6J4BpAWzCEX7oua0T5faAfvnULJsJ4mSJJGe6UtgbkgLAzLbb0cYLnKk8+aGpRsjHkNT/Gkry21KRylTOiZGxU4AfJEficFixhBSZm6KjzjiCObMmcOUKVO45557GDhwINOmTWPixIkZ2Z9wBcnvygiO6o70I/VctspgaawD6rJly7Asq571PmzYMPr168fixYsbNFjWr1/P9u3b670nOzub0aNHs3jx4gYNFsMwMAyj7nVVVWbV/ITbucuaBQqG3blzQoqTKiNyWp4ZLMky/mAEfzACPXY1HFu4cCF/mfYCy5YtY9u2bXs1ZXz11Vd54oknWLZsNWVlZSxfvpyRI0eSNAyiVRUkopXEoxWYsQqMWBVWjQHkJqsRNZ4fxYqh2jE0O4YvWYoe24TfjRMQcc/7U9Np51NnDpR6xlUmw1NmzGL1V6UA+IPp9eg0pIWR6X47wnZRgp33uEo3RiyOrqQvwbgpHaXLL788IzomxpZvAfCZZW0ef6fAiiOkzPX/+tnPfsbPfvazjG1/d+zSBE6ViX//zAvTdRQtPus11QF1+/bt6Lq+l3ZHjx492L59e4Pbq52/Z45LU+954IEH9uoynElc0bk9LHlBl6jZeccHUGwH6BFOQ+11DbFYjJEjR3LZZZdx7rl7t06PxWIce+yxTJgwoU73QJIk/H4/fn9P6N728IZwHRKxKnLveBe/bmc8l8aKWVRbXmzajNloWem5+DWmhTF//vyM9dtxXU/JV87q3L/bdGImYmha5kLcu+soQWZ0TIzKHQD48grTMuYOx4wj5I5PO0gHUm3yeue+d20TLTrD1nZAfeGFFzo0t2TKlClUVlbWTZs2bcro/txOrsMS0CRst/O6AG3LpsQN0yM7fXob48aN49577+Wcc85pcPnFF1/MH//4x7T1HGkISVYwthRTnuzBwUdlN7nu1KlTkSSJG2+8sW7e9u3bufjii+nZsyehUIhDDz20yQqCUPcg5159MADvPfZlm8ffnBZGurV4dsetqClpzoAsf2fFSMbxZbAr8O46SruTTh2TpOmFnv0DMt8Lp12w4wi54zuspwMlSwcJT5L/R0qLPCzNdUCdO3cupmlSUVFR766suLi40YS92vnFxcX1DrTi4mJGjRrV4Ht8Pl+9ZLJM47ommpbTbvtrKbqqYLvtk0PRGkp2FiOQ6ZH3I4l778bqdz/FJ+cx8LTGDaPGEtR/+ctfUlFRwRtvvEG3bt2YNWsWEyZMYOnSpRxyyCENbit3oPcdxg27zWO/5pprmDVrFq+//nqdFgZ4IdlAIEDPnj0z1m9nx+OewaUV/DguFqlgJOP4fOkzWG6++WbGjx9P//792bp1K3feeWedjhLsyh8sKChg8eLF3HDDDW3WMTGqvFCQLy+9Cdgdhh0H5cfxGxQ1DXDdZNvPDZ2VFt2W13ZA3f2O6/DDD2fixIl1zzVN44MPPqh7z+rVq9m4cSNHH310g9scOHAgPXv2rPeeqqoqPv3000bf096ITi7N71N92K6ccWnx1lJcvBWA7t3Sr+/SkQjHZs3aAPv1KUFtpAPv7gnqezY9++STT7juuus48sgjGTRoELfffjs5OTksW7as0X2qNVU1FVGbWHGsTeOfPn06lZWVnHDCCfTq1atuevHFF9u03VRwkzaSLhM8/Edy4UsBw4yjB9KXKF2rozR06FAmTJhAfn5+PR2l1atXc/bZZ3PAAQdwzz33cNttt/HQQw+1aZ9GdQUg0HMal6noSkhOAqF0faXl5PflFD/yBSjyj7ZCCFroYUmlA+rll1/OTTfdRF5eHllZWVx33XUcffTR9RJuhw0bxgMPPMA555xT5ya/9957GTJkSF1Zc2FhYb0kyo5EuHanDgnpqoTtqp1WjK24pASAHj3Tp+HRGdi+6EOq7W7sf0LjugqNJagDjBkzhhdffJEzzjiDnJwcXnrpJZLJ5F5iWrujBjSOP7EPC/+7mcUzvuXkyUe0evytMXDTZhQ7An1QNvKPtJoBoOz7jbz/6OMAKIpCafVmeg8a1sy7Uqc5HaVM6JgY0Sp8ioOkdY5QXvydd4iv2AmSAkLGrMpGCA1JMgEHSfImpN2fu0iyQJJdSJyGHtlJ5/g0rSOxuozSGd/gG5RNwRUHo+b/ODxGDZF28YhHHnkEWZY577zz6gnH7c7q1avrNB8Afv/73xOLxbjyyiupqKjg2GOP5d133+0UGiwAQjidWulWkV1s0XkVZIvLKtEIkNftR5KoV8PqRd8T1npQOPqEBpc3laAO8NJLL/Hzn/+c/Px8VFUlGAwyZ84cBg8e3OR+h501iLVfl7B6QzV931zP0J9ltgNsOnBtl+8e+Izs2K6y8uT3FXzz0DIkXQJNQdYVZJ83KQEFxa+iBFTUgIYWUlGDGlpYRQvrqO0g0tdWNnyylE3F39C721Bsy6BHzkD2O7ZzeI1bixGrxqd2Hk9u/IudmNE89NBOwMUXrAZJQusTBgeEIxAO4NY8d2VwZIQLwpUwYiNxMOnKwWprawxJk+n264ORMqAn05lo81V4z0oBv9/P448/zuOPP97oe/a8S5MkiXvuuYd77rmnrcPJCELYndpgUWWv+sZ2BZqSnh+sEALHFTjCxXZtbNfFchxs1yFp2Qih0C83ByWFA2RHZZzusoWsdV4vVUtxqkpYu7U3w4cnd2Xn70Ztgvq8efMaNbzvuOMOKioqeP/99+nWrRuvvfYaEyZMYNGiRRx88MGN7lvzqxz/i2HMnraCFf/d1CUMlvjORD1jxZQlbAnU0gSyECgCZATqHga3AKyaaXccIXAAV5JwpZpHWUIoEigyQpU8LQpNRqoxhiRdQfbLKD4Vxa+w5qvvWGusxe/zoauaN+k6o08+hp5DWq4cvCfJ6moUSeOCx//a5m11FsykQdRQeOaSU5ElCUWB4WOO5bDLb8/I/kQyibOjGFQdSdVA83nPFRkUCeHK+LJLyZ98aau2v/m2jyCU1/yKnRi9XwRhuVjbYui9098TqTPRea/CnYhwSSnhtXMoX7m0/oIau6uxS3ajAq3CJZHYiG1XEw7vX2PAuSAEQrjehl0byXVBOFDzaMSqeS/o58P8vDpjwhUuO+MygX55jJn5FAIXQ9kAgGr3qhGUcxE1EwjvUarZD27dOnXzJO+11ITCLMCTx7/FmIH9mvv62B616K6lr6QZvNyQ2vJN8PR8VqxYQV5eHv369aOsrIyNGzeydauXP1Mrkd1YImlLWf/OXAzRi6HjGk6ObS5BffXq1Tz22GOsXLmS4cOHAzBy5EgWLVrE448/3miDtER5kvcf/oKNO5P4ZDjouK7htUrsiAOgnLUfvY5ufMyu42InbeyojRW3sGIWdtzGTlg4cRvHcHCTDq7h4JoOwnAQlouwHE+O3BZIjotsCiRXIAuBLPCMIkDZzSDapm6kXKmmt+QnbiaoTFSxw65g+QvfoQkFTVJRJQVdVlFlFV3R0BQNTVXRVQ1N09E1HV3X0X0aus+H7vPhC/rQ/T5KSkqxVImd27YSjmThCwbTJhrXUYw851J8wdk4to1r23z//VY2fruSwzK0v7K//4tEWVMex/4EC4pavwNXIOmd31u3O7HPtxP/aieyT8E3OIfgiAIkv0L8yx37DJZ9wMB15fgqdmJpm7xrfCMWimjC2VB36a9ZRxVeRrdUuny3BVLNcgkkCSHJINc8SjK5ZWWM0TTm5mSjSDKarCJLMr3DKq4bRlVyUCSFbXYxNgn6h0aiSAqyJCFLCookI0sKsiSjyHLdvLpJVuqWy5KMIik168l17y2NVzO/5B/YsUGMSLGvTHEcevjTm7m+dOlSTjzxxLrXN910EwCXXHIJzz77LG+88QaXXrrrrqtWgPDOO+/krrvuatO+hevy5acWPbOKydvvpw2uU5ugvjuXXnopw4YN49ZbbyUe9y7ge17AFEXxNEoaYfuKnWzcmWTUwXkc+asD0UJdQ8fErDYBcF4vYvUbRex399Go+t6nH1mR0UM6eoY+l2M6mDELO2pRPGM5EV8PLvn9r+uWF322iu0/bME0TUzDxLQsLMvEtEwsx8ayLeJmAsu1MV0LWzhY2NjCafj4HzyCx5+skWIXAkm4SEJ43iRZ5ucXTmTgsPTltWSa/JEncMzIE+pe7/zNqahp8uo2hBPXUfUycn7iR9gW2DY4DsKxwHHAcdAPb30eFwIkX9cxWOyyJOX//h59QBauI6h4vYjKd39AmC72jkTzG+ji7DNYUiAo5cAR56Gc9kCHjuOLmWPJ3/o1L5z7UfMrZ4hNFaXMf/0fnDPkTML+1EI8Owyd0d3Sqw1wwgknNJkA+qtf/aqu62u6Kf54Advj/TjjnMYNi+YS1C3LYvDgwVx11VU89NBD5Ofn89prrzFv3jzefPPNRrcbrCkD7juioMsYKwA9Du/B4kVb8JUk6KlKHZZrpegKAV2BXD+mk6BbqL63bb8jh7HfkS03IFzXxTYsjFgCI2ZgxJPEq6rZWbYFwzFIJhMYySRGMulJP5SXU2q5bN24oUsZLHtiOxJaCxKnq2e/ibEpgSTj3ZfJeLWqsoQk1TzKktfET5GwjRyChaX4T8pAa4ia/mtyFzJYhOV5qsNH9SI4qjt2eZLY0mKcsiSRE/s28+6uzz6DJRXMKOgd72oTio4m0htaaSl9c/IJOcN4Y9NTfP3CGsJ6iLAWJqKHyPZHyPaHyfNHyA9kkR/KoiCUxTbbT/dI50nUaytffbCebD1M/5P/p9Xb0DSNt99+m8mTJzN+/Hii0SiDBw9m5syZnH766Y2+L9zDK4uNlXatuynVr3LclCP57tHlGJujKB2cNOu6LhZJsrObFvxLFVmW0QM+9ICPSLdd84dwUIPrL/94Ea/P+4D87l27PNhyQW2BwRL9ygThoupVICSEkAAZhFzzXEIIuW6ehIXeLzNlx7V6JbK/61wGtR4h/AfmU/7qWtT8AHrfCNmnZLD7cyej6/ynOhLHAqXjE0aFoqE2ES5oL2ad9Q8ue+tmthjLcIwErmSA3IQHZRiYwUvab4AZxKquZn1xLw4/uKTBZNum2DNBfciQIU0q2zZEIM+HBMTLu6aapYhaWGrHVzKUFFeAJMjLz+mQ/ZfXlPp3S3MH7PbGdkDTUr+MCKETHmKQddkVGRzVbvszTRACVG2v49WtSQKXAl3rMph3wVBK/t/XlM1eRY/fHf6jrwzana71n+oohANyJ3Abqj400fEGy6D8Aj785cx681zhUpGMsjNWxc5oBSXxakoTVazbsYI3djzPqH4/Dm2ADe8vwBZBhpzShrh5G5AVGZ8iEavsmgYLSRs6QZJj8RbPYCjomd8h+6+sqAAhyCvo3iH7TxeWA6qe+s2cEHq7Jbm6VZVsu38xglqxPgdwkKjRYkEAYbTFN2N+vgUkHSHrIGsg1zxXaicfqD7vueoDzYe2/TXc/NG43Q4BLQC+AJIeAF8QyR9ECmWj9B+S9h5jsq4QGFFA5ZvrcCoM1LzOIf/RHuwzWFLBtT1hoo5G0VE7qZqtLMnkBbLIC2QxtNuuktClK4p5Ywckq1U2FK0iHMkmHMnG5w90Ss2Y5vh+2U56BBWyhjScbNse+DWZRPWehb5dA8V0sTvBCbak2JOY79m7WzNrZoaq6ipk10FWOsF5pQ3YDmi+1P6fwnUR6EiNhAO3bNnCrbfeyjvvvEM8Hmfw4MHMmDGDww/3+hYJIbjzzjt5+umnqaio4JhjjmH69OkMGTKkwe25ZSUIQoT7rUcrCHrS9baNsB2E4yJsF7c6ihLMR7ghJNdEcgzPo+4mkO1KsExwTSRheY9YILxHmThKdC388EKTn9shByH5QPIjZB/Ifq/houJHqAFQ/J7Bo/pBC3rPNT/oIe/RF/IMIS2AUdWN6s9M3JiF1jfyf6oXF+wzWFLDdTuFh0VqpcFy11137dXdeujQoaxataru9eLFi7ntttv49NNPURSFUaNGMXfuXAKBlnlGXNdlbdl2snwBugWz+G/NnezV8/PA3VV+qOAQJkGYBCFNEFIcwooLsszh3eGGK37d2C46DKOsjB9KChlzeEmHjsPvU0jEu2a/EN0VuJHUkoUfeOABXn31VVatWkUgEGDMmDE8+OCDdb1wysrKuPPOO3nvvffYuHEjBQUFnH322fzpT39qNjelrLQchExuQcdIhsVicbSuZ6/vRWFgJFmluVTNmIWkKUiqXPOogqYgaWrNpNc0glGQGvDIlJeXc8wxx3DiiSfyzjvvUFBQwPfff1+vncWf//xn/v73vzNz5sw6RfRTTz2Vb7/9tkGtI5Hw8rwChw3GN3pMRj6/cFyEkUAk4ohkHJJxhBGHZAx39WKEa4OZ9HoWWQmwk2AnkJwkOEkkcwe4BpJIIrkGEjXPMWvUeusjiQGYg/+FPjCb8JjC/1PhINhnsKSGcEBO7atq7i6hMa/Cn//8Z2655ZamN6623sMyfPhw3n///V2bUnd9nsWLF3PaaacxZcoUHn30UVRV5csvv2yVZsQf58/k9S0P170WQgGh8cb5fYnF40RjCaLxBNFEgtiq/xLL2o9qrRsxyyVmwdsVvVlUDWd89xWhcIRgOItQKIKqd3xFzLp5C3EJs98pmTn5pYo/oBKt2vtk1tmxYiaaBFZuaneFCxYs4JprruGII47Atm3+8Ic/MHbsWL799ltCoRBbt25l69atPPTQQxx44IH88MMP/OY3v2Hr1q288sorTW47Ho+D5PKnu+9DRkVGRZG9SVM0VFVj+IHDOfFnRzW5HWj+mL/rrruYPXs2mzZtQtd1DjvsMA4cNIABfdsuTteRuLbDEQU/Awuia6IIoSLQgKZv7uSsvfspPfjgg/Tt27deF/DdG2wKIZg2bRq33347Z53lVQw999xz9OjRg9dee61OtmB3ag0WKYOK6ZIiIwVDEGygR9Shx7d6u0IIhOUgknFEIoZIRFGfPRz1sJ+Sf+aBbRhx12afwZIKKYaEUrlL2LZtW733vPPOO1x++eWcd955zW5fknU0wHEslBYmAauq2qhg2qRJk7j++uuZPHly3bzWdnT9vrwIycni54OuocqIUW1G2S+3HyMOaejEf/Fec6Y/O5MHV3Xj5Jmb6s33YRKWDIKyRUi2CCkOIdUlpArGDuvGOWe3vmInVdauqKAwUkG4f+OdmdsDPaBilSU7dAwtwagyWPPwF8iOSzbg65aa1+7dd9+t9/rZZ5+le/fuLFu2jOOPP56DDjqoXtLyfvvtx3333ccvfvELbNuuZ5TvydjxJ/DZomySSWM3zRUTyzaxbYuy6HaWrzCaNVhSOeb3339/HnvsMQYNGkQikeCRRx7hqeee5+Zrfsvrz8/E5/ej+3z4/QH8gSD+QAB/KEgwGCIQDhPJye2UgnN23PsNJg5xGfLzcXXzheUgjCSYSYRhIizvEdMASULdf+8L7htvvMGpp57K+eefz4IFC+jduze//e1vueIKLzl3/fr1bN++nZNP3nXsZWdnM3r0aBYvXtywwWJ4Rr3UQi/xnjTnoT7hhBNYsGBBveVXXXVVo+KPqSBJEugqkp4FWTVeQElGKl/b9Bt/5OwzWFLBTS3ptrm7BGAvo+H111/nxBNPZNCgQc1uX1K9O1PLiqMoLSvH/P777yksLMTv93P00UfzwAMP0K9fP3bs2MGnn37KxIkTGTNmDEVFRQwbNoz77ruPY489tkX7AChLluCXCrjthIta/F6AK38xkZ989yWxWIxYIkksESeWMIklTeKGRdR0iJsOUVMQt+Gdir6UfbmFc85u1e5SJlmyg83lhRx7VHlmd5QCWs42sno+z7K3n0OWfMiSD0X2Ics+ZNmPLPtQFD+y4kdWdRQlgKL6kdUAiuZH0b3Xii+ArPprlvmRFQ1JVdLuZi79upTcpE21T6EypDHokNYlmtb2H8vLa1xKvbKykqysrCaNFYAevfMZf0HjhufD905HSaFcN5Vj/qKL6h8LDz/8MM888wwbd+xEXbu+2X30Cge56ubfN7tee2PGPINF2UOPSdIUJC0EpN6Zet26dUyfPp2bbrqJP/zhD3z++edcf/316LrOJZdcwvbt2wHo0aN+GXiPHj3qlu2JMJKA3maDBZr2UANcccUV9VrLBBvp3t4mhv0MjKr0b7cLsc9gSYUUq4Sau0vYk+LiYt566y1mzpzZ4PI9kVXPtWlbCfCnbrCMHj2aZ599lqFDh7Jt2zbuvvtujjvuOFauXMm6desA7y7ioYceYtSoUTz33HOcdNJJrFy5stGEtsaotksJq62vvFBUlQMPTl3o+xd/ehLblSha/TUFBT2I5HRLe1Y+wIZ5C3DJZ9ApLTfi0o3S62uCweVYyQNwJROBgetaCMn0XssmrmyB3ELNHiEhuRqyoyMJDUnoyEJHRkdCR8GPLAVRpACKHECRgyhKCFkJoKohFC2EqtU8+sKoegjVHya+rQyf5ND/+sMJtrKTrOu63HjjjRxzzDF7CfLVUlJSwp/+9CeuvPLKVu1jdww7QX64oNn1WnrMm6bJU089RXZ2Ng8/8gh5ubkkYjHi8RiJaJREPEYyHsdIJEgmE8z/dCmW3TkTrM3qmnYLgbaHa13X5fDDD+f+++8H4JBDDmHlypU88cQTXHJJ6yQRRNICdCR/2w2WpjzU4Bko6Wj50ShCQMka6NMx1YmdhX0GS3PU6p6kEBJq7i5hT2bOnEkkEuHcc89NaSiS4p0YbDue+viBceN2uWtHjBjB6NGj6d+/Py+99BIHHHAA4Lkwa6XsDznkED744AP++c9/8sADLVP3TboV9PY17y1KF5oMH8V6c9KMjcBGdEwK5Bj5apI83SHfL+gWVDly/96cdPK4ZrfXGEVfVdAzYhDq3XHVQbU4SiWO0Y2jfvZ6k+u5to1jxrGNBK6VwDETOGYSx07iWAlcK4nrJHBcA9c1cUUS1zZwnCSuY9RNjmt4y0QSx41jSaW4rqe/4zgJXNlA2AYYjeRX5YF2VB965/631Z/5mmuuYeXKlXz0UcMqz1VVVZxxxhkceOCBbW69AGC5ScKh5sUiUz3m33zzTS644ALi8Ti9evVi3rx5dOvmVSiFsrIIZTWc/LvgkyUEA52zl7BRWQ2AHmm7N6FXr14ceGD9UNEBBxxQF/KrNQaKi4vptVtLkOLiYkaNGtXgNr2QUAgpkLqnpzEa81DX8sILL/D888/Ts2dPxo8fzx133JE+L8uOVfDWTbBzFZwwJT3b7KLsM1iaw62pxkgh6baldwn//Oc/mThxYqPdfPdkl4elZQbLnuTk5LD//vuzdu1afvpT7wLc0Mli48aNLdquEAJbqqR7oPk704ZorioEYPv27dxyyy3MmzeP6upq9t9/f35/+a84/LBR7CyvYGdlgp1VBmVxh9KEYENU4d8lvXhyo8vJnz5BRIeILhH2KUR8CmG/RlZAI+D34/cFGD78YPK71++RZO7cwqbyvhx1VNu+93RhO+VA8xcxWVWR1Sy0YOYveK7j4hgx7EQUO1mNbUSxrSiOEeO7rfdjaLFW52Fce+21vPnmmyxcuJA+ffZOVK2urua0004jEokwZ84ctDZ2BbcsG1cyiTRiROxOqsf8iSeeyIoVKygpKeHpp59mwoQJfPrpp3Tv3nR4zJEkwuGOV9luCKekJiSUYkFCUxxzzDF1DUprWbNmDf37eyquAwcOpGfPnnzwwQd1BkpVVRWffvopV199dYPbdM0aYbg2elia8lBHIhEuuugi+vfvT2FhIV999RW33norq1ev5tVXX23TfonugA+nwrJnIW8g/PJ1GHRC27bZxdlnsDRHrRR+CiGh5u4SdmfRokWsXr2aF198MeWh1OawOHbbEi6j0ShFRUVcfPHFDBgwgMLCwgZPFrt7ZlJhR3U1kpKgd6R1cuPNVYUA/PKXv6SiooI33niDbt26MWvWLK674SaWLl3KGaf/pMHtbt+ygT//610qDNgSl6muUql2NKLCR7Xw49RUNZyYgENe/Jps9WN8qolPs/DpLo4tcBjAoJNHtepzpRvXrUCWc5tfsR2RFRk5GEELRoD6Bt/KDS/jUtzibQohuO6665gzZw4ffvjhXrkh4F20Tj31VHw+H2+88UbKxn9TlGwrBwly85oPu6Z6zIdCIQYPHszgwYM56qijGDJkCM888wxTpjR+xxwtKwNZYeunnzBr/ntoqkpMOJQaCYYWFBIIBFB0P5rPhxYIoPoDaMEAaiC463UggBYIogb8SFXV+Pv0RS8oQA4E2hw6dcotFEBNgwr4pEmTGDNmDPfffz8TJkzgs88+46mnnuKpp7zGkZIkceONN3LvvfcyZMiQurLmwsJCzj777Aa3KUzHKxNW2yZJ0ZSH+vLLL68Xgjz44IPp1asXJ510EkVFRey3336t22nZenj6RC8UdPKdMPo3nmDd/3H2GSzN4dYYLFLzB3dzdwm788wzz3DYYYcxcuTIlIei7J7D0gJuvvlmxo8fT//+/dm6dSt33nkniqJw4YUXIkkSt9xyC3feeScjR45k1KhRzJw5k1WrVjVbGronq0q2ANA/J7UuznvSXFUIwCeffML06dM58sgjAbj99tt55JFHWLZsGYccckiD2+3ZewAP3/ybBpcJIUgkkyRiUabf/SVOyGZovxhmwsFICgxDxrQVDhywlaw+HR8OAnDlCjS5lSfCDsARJchSywXarrnmGmbNmsXrr79OJBKpS67Mzs4mEAhQVVXF2LFjicfjPP/881RVVVFV5SUlFhQUoLRSlG3ndk9Urlv35o3Clhzzu+O6LobRtFpxdIt3PGU7LpIskbBMSh2v8mV98RYkAY4kcCQJ0RIRRiGQhUBxBQqgIKFINZPsdW1XFAVVUVFVFUXVUDUdVdfRfD4U3Yfm9+PGQvTnCMzviqgs3Ykc8KOE/EjBAGr3HLT8SMpDOuKII5gzZw5TpkzhnnvuYeDAgUybNo2JEyfWrfP73/+eWCzGlVdeSUVFBcceeyzvvvtuo0aqMO0GtUzayu4e6oYYPXo0AGvXrm29wbJsBiTK4ebvIdy11ZDTyT6DpTlqpfBTMFiau0uopaqqipdffpm//vWvLRqKVtNd1I2Xtuh9mzdv5sILL6S0tJSCggKOPfZYlixZQkGBF7q58cYbSSaTTJo0ibKyMkaOHMm8efNafLB9X+qdYAfnF7bofY3RUFXImDFjePHFFznjjDPIycnhpZdeIplMcsIJJ7RqH5IkEQwE8Pt8BG2J4EE9OOqq09Ix/IwhyVVoSufysDSJUoYm79/it02fPh1gr//tjBkz+NWvfsUXX3zBp59+CsDgwYPrrbN+/XoGDBjQquGW7qwAYOvmYhzbJRgOUNArj3DW3jkJzR3zsViM++67jzPPPJNevXpRUlLC448/zpYtWzj//PObHEdVjYF2ym+uo88RTSdbOraFVVWNWV2FVVWNFY9hJRLYyYT3GI8R/fprpNwcXE3HNhJYSQPbTGIbJrZlYlsWtmXiODa27ZCwTJxkAke42ELgCBcHcCUvVFUQ6Ef/Xkdgf+Oj+hsDMADvmBWuQ68/HIaak7rR8rOf/Yyf/exnjS6XJIl77rmnXjVOUwjLRZLSn7C8u4e6IVasWAFQL9emxQwZCx//DRb+BU6b2imESzsD+wyWVEnhDiaVuwSA2bNnI4TgwgsvbNEQAtEdAKilLavFnz17drPrTJ48uZ4OS2vYVOm5/Yd2692m7UDjVSEvvfQSP//5z8nPz0dVVYLBIHPmzNnrgtVSiksT+IREXkFmOsOmE1mvQlfaJinfnNjZ7vzmN7/hySef5JFHHuHGG29s0X6EEMhaOT6l5WFC0YxI4gknnNDsOq1BrinrXvj5e3XzFOHnjrv3Pj6aO+YVRWHVqlXMnDmTkpIS8vPzOeKII1i0aBHDhw9vchzVNQ0Ss3o3fzwpqoaSl4e/iZLvdOK6LnY8jlNSDXETN57AjSVxE0mqF6xFkvsjzI6tbhKWiyS3XRG6KQ91UVERs2bN4vTTTyc/P5+vvvqKSZMmcfzxxzNixIjW73TAsXDGw16y7WdPwZUfQmHDHuT/S+wzWJqlZSfE5u4SAK688spWlV5a3YcBEI90zg6vW6PF4Ork+tue5NlYVcgdd9xBRUUF77//Pt26deO1115jwoQJLFq0iIMPPrjV+9vwgxdK6Nm7cyY41pKMR5FVA5+v9aXjqYid1TJnzhyWLFlCYWHrvGaJWCWyliTg75y/2Yb4ybgjOfCQIUQrYyRiSd55911iRmWj6zd1zPv9/lYnX1aXe5o/rz3+OLqiomsquqah+3wMHDGCISed1KrtpgNZltHDYWggIdjcWI6xDpRI26tz2oKwRVoMlqY81Mlkkvfff59p06YRi8Xo27cv5513HrfffnvbP8ARl0O4B7w4EV6YANcvB1/nPj9lmn0GS3PU3cF1fM8G2ecZAjYd37G5IRauK0KNRNrc1LCxqpCioiIee+wxVq5cWXd3OnLkSBYtWsTjjz/eJmXJDWs99/u3Ly5l/eufoekyml/BF1DxhTT8IZ1A2E8gK0AgK0A4N0w4N0wgHEBW2k+FtLrC82L5g633sKQidgaeF+a6665j7ty5nHHGGa3aV0XJZgBCkfSECduLgp65FPT0DLj/vh/CsVuoaZMGBh12OOtfm0PCdam0bGwDLEkiqWmsfe+9lAyWplRaN2zY0OD/HTxPZnMhq8ZwkybCsZADHZskKmyQWqpF1ABNeaj79u27l8ptWjngZ3DlAnj2DHjtavj5/2ZuX12AfQZLs9QYLJ2gs7CqeuV5rt10sl5HIalVuFbrvSvNVYXE415Z8Z7lsYqi4LptM+LWl2/lB03lMCdOvErGdRSEKxBIgFITC7eA6r3GLGGBZCHLDrLsIqsgyy6qLqP6FFRNRtVldJ+C5lPR/Cp6QMMX0PEFdXxBH76QH3/YTyDsxx8J4PPrDSrORqu8sGAo0vpEvFTEzlzX5eKLL+aWW25pNnTRFNUVWwHIyu1aBsvuJJIxAv729xb0G30kl44+cq/5/2/y5BaViDem0tq3b9+9WoU89dRT/OUvf2lxheDuCMMBt+PF7oQNZrIvxX+cDZIAJIKDIXLx3jL+nZpQAQw8Hn74pKNH0uHsM1iaoxN5WFTNy4Z3nM5psERCcfLa4Ppvripk2LBhDB48mKuuuoqHHnqI/Px8XnvtNebNm8ebb77ZprFvicdZEhA8+ee984psyyReGSNWESVWGSNemSBelSAZTZKM2RhxGzPpYCUdbNPBtgSuI5OIWriVDkLIIBQv30ICSZLxEhSb+T8KGyQbSbKRJAdZcQn2WEPhmLYZLKmInT344IOoqsr111/f6v0AxKLeBTEnv+15TR2FYSfID7UtZyidxISgVwvKtxtTaVUUZa/5c+bMYcKECW3SfuksBktozADkpevqXseKB5FYt5HU04A7mK3L4Y3rYftX3utjbujY8XQC9hksqdIJPCy1Zc2d1cNiigpy9NY1TYTmq0I0TePtt99m8uTJjB8/nmg0yuDBg5k5cyann356W4ZOcdQit5FEfFXTyeqmk9Wt7ZU5ruNgJpMkowmS1QkSsQRGzCAZMzATJkbcxExamEkbK2ljmw6W4WCbAtsSKPpOACLZrc9haU7sbNmyZfztb3/jiy++aHN4LxHfjisC+DqpWmtzuI6LTZLs7Jb17sokCVkmHErd49OcSmsty5YtY8WKFTz++ONtGp+wXIRoe+5IW/Ef9xP8x+3SZnIf/CeumbnOzWklUQHP/gzyBsH5M6H/mH3lzewzWJqnE3lYFKXGYOmEHhYhBI5cRbdA6+9EU6n4GDJkSINCfG2lNCno5s/8/1hWFPyhEP5QCFqhr7forQ9JmAq6v/V3wM2JnS1atIgdO3bUu6g5jsPvfvc7pk2bxoYNG1Lel2nuwKULlWDvQdmOaoTskNstp6OHAoBjmiR1PSUVXmhepXV3nnnmGQ444ADGjBnTpjEK2wXaP+enWVw3JXmKTkHVFjCjMO5Bz1jZB7DPYEmBzpPDUuthEU7Hu1v3ZHt1FZKSpFcXvQsodzQODHf8/7g5TLMUt42Kl82JnV188cWcfHL9TsannnoqF198cV2/qVSxnR1A671B6aaibBOrv34eRfahahE0LYymR9B9EXRfFr5ABF8gC38gC1X1s36t154inN32BnrpoHLzZpAksrqldmPQnEprLYlEglmzZnHHHXe0eYzCEcj+7my88TXABVwkXJAE/v1DFFzddBVlxnAlJLlzFizsRe3Nm9L2xpI/JvYZLM3RAuG4TKNotQZL+tUb28r3pV5yZd+srlO+Wott21RJfgobiwk1QSr9j6666iref/99tm7dSjgcrltn2LBhLR+rUwFS27RimhM7y8/PJz+/vpGhaRo9e/as97lSwZVKQOo8HsFvlj5HQpmBa/tQtKZbXAhX4YcNI4HhZOV2jlBC5Wav6iq7R+vaXzSm0vrKK68Qj8f55S9/2eYx5pxxEFUfrkL2CYQjwBEIAcKIkPw+mvJ2XNfFtR1kVWl1H6rdEa4Mevp1ezKCGfMe09D24MfEPoOlOTpRSEhVOq+HZX25Z7AM7ILVIJu27sCVFPp2a3meRSr9jw477DAmTpxIv379KCsr46677mLs2LGsX7++xfLxjqhCa2PaYKoCh+lA9m9AuJ3nLtEwt+O4/Tlp3HtYSZtkohozUY2RrMIwq7GMaiyzGsv2mjcGA15lxsBBfTt45B6VtYnofVs3nsZUWp955hnOPPPMOvXrthA+bgTh4/YWTdv0u9pqndR47qGn2RD3kraHZg3gwpt+1aZxCVfuOoKxRR+AHoHuBza/7v8h9hkszdKJQkI1HhY6oYdlY6V3YhnSBatBijZ5pcIDerVcJTSV/ke7iwQOGDCAe++9l5EjR7Jhw4aW9xqR4yhK243CVAQOd6cleSv1SB6NYHvr3psBbGcHEgWomoKqKQQiPqDx8ErxG0k0rRxd71gRtFqqysqQhEhJ/RaaVmmtZe3atSxcuJC33347U8P2cCUkNXWDpSoZpY+vO6Zj8kPlFl6a9hyyLCNJMoOGDuKQsUe1aPfClZG6gsHi2PDVS54Gyz4PSz32GSzN0YlCQrKseqlsndBg2VDpxfqLo6X0yuo8FRWpsHG7pyg6uF8ben/U0FD/o92JxWLMmDGDgQMH0reFd8lCCCQtia53npyQ5nBFCbLUuvBFJhDyTjTpoOZXrCFaHcPv7zwJpNWVlfhME0VN7dTdXB8xgH/+85/06dOHsWPHZmrYHkKCFhgMAkFBdh69+vRm+dcrKI1WIHCptuP8sGQzhf36kNunG3o4tXCdcFUkpZPlsJSth02fQbLSM04CubDqLShfDxNmdvToOh37DJbm6EQhIQBbkhBux5cM7sn+eUP4rBIivs7fi2dPNpVWoQjoWZDTpu001v8I4B//+Eddt9mhQ4cyb948dL1loRIzEUf12/j8XSexWY18g2K0TiW3OYq+fY+dxZ+hKH4UNYiqBFDVAIoWRNMCaL4Qmh5G90Xw+UJogQiSVooup25ARaMGwWDnOPYBorEYwRaIJKbSR+z++++vK3HPJAIZuaXKs5LEkWcex5FnHlc3a/7/e5OFm5cyffb/o6+/B5dPvjqlTVW6MpZZTHzFB/QcfhyK1oGhylgpvDsZvn7Je634PO0a4UKkF4y9F3qN7LjxdVL2GSzNUhsS6thR1OLQOXNYtsd2IITCgNy2x8Dbm22VSbLF3gq6LaWx/kcAEydO5JRTTmHbtm089NBDTJgwgY8//hh/CwTAKks3IauCUKhr5AnVqg8LfXlGtl+04Y/IvhJcI4CkmMhK84a8okPQ16fZ9WqJx12iUT9LlszC5wvi94cIBLIIBCIEAtkEAjmoarDNejWpEjNNgmlIQO0YZFBS/548nem91//Jxaex33fDeO2t19mULOZv9zyEjIwkQa+cnpx60RkomoqqaciagqJ6bp031fVURZPw2iJO//4rjjx/Uto+Wcq4Lqx4Ad6/0zNOxv8Nhp8L/ixwLE9/JdStU6QgdEb2GSzNURsS6iQWiyNJLNg0n3tfOqJmRFLdb1vyXnnPJQkJibM3jaen1RchywgErixwJYGQBMgSiuId0IqioSoqsq6g+DXUgIYa0PEFA/hCAQKhEMFwmGAojNqAbHxxbCeyk4XSZbLadrEjapOrZab/US3Z2dlkZ2czZMgQjjrqKHJzc5kzZ06LOnZXl28AIJTVORJAmyNW5XUbzg5MyMj2Fb8nonfyKV9iWy6WYWIacSwzjmXE+efMZ3l+9sts2boNBAwaWMjlvzydq649t24bixcv5rbbbuPTTz9FURRGjRrF3LlzCQS8Mma/X6KyUubdd9c0Og5JclAUB0VxURSXwsIAv/jF3Y2u3xZirktBoONLrIsWLGDjN99QsbOEzYk4MhKKBIokoUgSqiShyDKqrKAqMoqiIHw5aGisfuEdVFVB0zRUTUVRNTRNRdU9I0PzaaiaRrWbaPC6rfhU+o8azNmcxVdLvvCqiVzBlyWrKSmt4utH9/hfCVCQcSSXI/sOYdmmVQi3A6qFtq/0ui9v+hQOnuB5USK7efsUDcJd74avPdlnsDRHbUioE+SwACgyDPHpbAx1QyAQ7C64tuu1qHk1ZsdoyoNVREM2spCQHAlJSMhCAleAKzybzBVIwkJzBEFHJegqKAggDsRxKKUar5OOi0tCMUgqJoZqYWk2E5w+nOZexNxHPgNdRvKrKAEFLaChhTT8IQ1/RCcc8RHK8hHO8qFrncO4KTElegVaZ7A01/+osfcIITCMlpX7Rqu8PKHsvOb30RmoKPVKcHNyD8nYPnyciqzI6IqM7lcJsSskOeqIYzjsqOMZMmQIQghmzpzJLbf/hZPPuIrhw4ezePFiTjvtNKZMmcKjjz6Kqqp8+eWX9TxtV1/9IJaVIB6vJJGoIJGoJpmMkkhWYxhxjGQc0zQwTQPLMigq2sTWrRn7uJ7KbbBjw65F8+fzvwsXIrkuAdOkl6qiugJHuNiOgyMESQE2Ak9CTsKWwNVtXFnFWVOMg4sjNR/a0poI2/QfNZj+owbXvR5bVsW6L9bgOA6ObePYDq7j1r0WwKiTDuezf3zPmg1bWPqn35HtA0WWUBUJVZbrjCtVUVBVBUVRUTUVVdVQVM+QUjUdVfOhaDqq7sdBoSph06tnd/zBML5wLsH83bygjg0fPQwLHoS8/eCSN2HgcXt/oH00yz6DpRmEcJAA6+ULieV71m/9S1vDFzqx52xR80eSqAsz7T5fCCThmRmS8F5789ya595jyHE5vsfBnHVGai3rN376HvkjDA49K/WGX5ZjETWjxBJR4tXVJOMJjFgcI57ETpjYcRPXsBFJBwwX2QRfPECvRDbBUgO/K/C7EALUBr6feM0UQ5CQBDsdk79Z2/DJLiEFIiqEdJksn0rYpxEJ6URCPrLCAbKyQt6UGyY7L5twVhiljd2SKxyNQyKtM1ia63+0bt06XnzxRcaOHUtBQQGbN29m6tSpBAKBFrcTSES3gt51PCxV5ZsACGelljOy4pPnKS75XxA+JAJI+JGlALIc8B6VAEpNnoosBxCuRDDQeJx//Pjx9V7fd999TJ8+nSVLljB8+HAmTZrE9ddfz+TJk+vWaUhnRtMCZGcHyM5uXmNo5sw72LrVoqJiC4FADpoWSIuGCIBr2yRaoHKbKUo3eYbopGuvI6t76z0CruNiGxa2YWIlLWzTwjIsb55lY1sWfYanbpyH8rI4+OTDm9mnwwB/FTuTKoZQUcwkER3itovtOtgCHFfCFt7kCBkbBRsZpwWXy8t/djR9Dz8VtiyDubd5XpXjfgfH/x7UzlPm39XYZ7A0g+nXKOvuQ7cFmrX7HXEDLsXdpOX3vvzVzmk4LiskCSQJIckISULIimfcSJLn3ZFkkBQqs3vgG3n5Xu9vDCG7uHbLknQ1RSM3kEtuIBdaXulbh+u6GAmbqsoksSqTRLVJMmZixCysuIUdtwluKOGAhJ9hukWZLYiaUGooxGIQlSXiskxClRGSA0Rrpl1IwiXgmIQci5CwCOEQkl3CCoRVibBPIexTyPJrhIM+ssJ+z/DJDhLJChPOClIt+emV07qLSnP9j/x+P4sWLWLatGmUl5fTo0cPjj/+eD755BO6d29Z8mwiuQNXkVDVrtG+zTS8iqmvV5+G+62GsAMIJ4hwg0giiEQIWQqhyBEUJUTC/gi0SmRrBEIkcanAZTsIA6QkkjC8yTWRhECSISdvUEpjcRyHl19+mVgsxtFHH82OHTv49NNPmThxImPGjKGoqIhhw4Zx3333ceyxx7b6M3ueMz/Tpj1dM8dFVb2Qkaq6KCpoKqiq7N2YmD3Q5F7oul43+fw+/H4f/oAPf8BPIOQnGPIjKnYgZJlIIxVo7cWm4u0EEglCWW37HcqKjB70oQfbptzcsn0q/Gryw616r3AcHDOBbcSwjQSOGcc2DOKxKop3lpIX0ti5uYh3vqnArd4OL/8KvpkDBcPgkv/AgNb/rvbhsc9gaQYhK3w7LMKokc+Sld8F3XgSyF/ms/nzRQCYfTYx6NqL2mXXsiwTCOkEQjo0kie6/aFXsaojPPRQ40aYkzSoqqiiqqyKqsoYVZVRqqrjVEUNonGTqoRJNGlTbThELZeYLai0JbZaEtGETFxWiSkahqICNtQFt4q9HUgyWz/8D39/63FUQJNkNFlBU1V0TUfTdXRfgKzuPRhz25317pib639UWFiYNn0LyywBLdBuCZ5tZfjh/8Oar8IYRgWWWYUjothU4xDDcaMIEcNhC46IgxtDCZYhi0J+Ou7ZJrcrhMC2DBzHwh9o+qL59ddfc/TRR5NMJgmHw8yZM4cDDzyQJUuWAHDXXXfx0EMPMWrUKJ577jlOOukkVq5cyZAhQ1r1mc899zrWrfsUw4iRNBKYRgLDqA0ZWZimhWU5WJZDIlGBmZCxDRdX2LjCwZXsZsXVcgs7TutICMHaigqGqBpKCxLG98RxHO666y6ef/55tm/fTmFhIb/61a+4/fbb637fd911F7Nnz2bTpk3ous5hhx3Gfffdx+jRo9P1cVqEpCiogTBqoH4fr3yg1ucpfT4XvllMaMEfIeyDsx6HkRfSdRTrOjctMlimT5/O9OnT60Skhg8fzh//+Me6fhVFRUXcfPPNfPTRRxiGwWmnncajjz5KjyZkpO+66y7uvrt+gtrQoUNZtWpVCz9KZnBr2qRLcvptu1QO2jbvI68KZecuXRR1a+eSznerTIQda3Idxe8jt2cBuT3blpBmJpJUlldRVVFNVXmU6sooldVxtm3ezAGRQpx4FkYijplMYhpJLNMkaSSpjsdIuDtJ7tjMqK1bCPfpmJCM7Zajup1DwCwVdF+Qg444t/kVa/hg/hCycpqvgJIkCU33o9H8BXPo0KGsWLGCyspKXnnlFS655BIWLFhQV8F01VVX1fVHOuSQQ/jggw/45z//yQMPPJDyuHcnK6sHo0admdK6c98+goB0GsePu7nefNOwiEeTxKJeN+9EPEnJmh+wn/kzyiGj6X9MBzbDEwJLktgpXH745BP6HnVUq0JeDz74INOnT2fmzJkMHz6cpUuXcumll5Kdnc31118PwP77789jjz3GoEGDSCQSPPLII4wdO5a1a9emRZE33RiGwRffbQAE4XAWXPYGdJF8s65Ci67Cffr0YerUqfWS2M466yyWL1/OgAEDGDt2LCNHjmT+/PkA3HHHHYwfP54lS5Y0+aMePnw477///q5BpSiK1B7UtkmXpPSPKZWDtq30v/YcXDeBpmVR9MTzSLHOpZzoJgRSO/Wa0QN+CgJ+CgpbrmPyyf13s2T5ZwR7tl1crrUIqpHp2PyFTGHbccAlEEivMajrOoMHe4mZhx12GJ9//jl/+9vf6vJWGupavXHjxrSOoSEcx0LRK/Are/+edJ+G7tPIyd/lPdpcvJPqLVvIu+v8tOXEtAZJlsmyLLYFg8x47z1+adsMqlF0bgmffPIJZ511Fmec4Wn0DBgwgH/961989tlndetcdFF9T/DDDz/MM888w1dffcVJJ53Utg+SZrZt28arr75KRUUFZx47Av9P/7jPq5IBWnQVbiqJbcuWLWzYsIHly5eTVZMUNnPmTHJzc5k/f/5e3V/rDUJV6dmzc93511JrsMgZMFhSOWjbiqJoKDXyzlJcgVBqwk3NeX8sy+L222/n7bffZt26dWRnZ3PyySczdepUCgtT1wkRtoKkdx4l0caIlpbiFwK5g4xpy7TYufos7Hhvnv18ForqoqiC8o29UXSLHvvvRFElFE1C0WRUVUbRZVRNQdEUVE1F070qB03XUHUdVffCXZruQ/P5PK+FL4Cm+5Db+WRbXb0SgFBwcDNrtg3XdTEMgwEDBlBYWNhg1+rdOxxniurK7UiySzBFTZ3EJq/1RXhgx7e+uPIPf+Cbt9/mP2vWoOityz8ZM2YMTz31FGvWrGH//ffnyy+/5KOPPuLhhxvOLzFNk6eeeors7GxGjuw8gmpCCJYtW8Y777xDQUEBV1xxRYtz0/aROq0+++6ZxFZUVIQkSfh8u37Afr8fWZb56KOPmjRYvv/+ewoLC/H7/Rx99NE88MAD9OvXr9H1DcOoVxJaVVXV2o/RLLs8LOk/gbf0oG0rUtwP3ZvuUFtLc96feDzOF198wR133MHIkSMpLy/nhhtu4Mwzz2Tp0qUpj0kIH0owtTEBLFy4kL/85S8sW7aMbdu2MWfOHM4+++y65ZmKe8eqKgh00B2T67q8+Kc3iO08nO5DN6EoYFtetSSAY2pUbpdxHRnXUXBtBddREY6K62ggdh+3Cxg1U+NIsoWk2Agho+oJTrmigIEHZC6Hq7JqBQDhyAFp2+aUKVMYN24c/fr1o7q6mlmzZvHhhx8yd+5cJEnilltu4c4772TkyJGMGjWKmTNnsmrVKl555ZW0jaExKktrK6hSM0CMrduRJJVAYce3ZfDn5KDVqDRHerTu4jx58mSqqqoYNmwYiqLgOA733XffXg0433zzTS644ALi8Ti9evVi3rx5dOvWeO+n9sQ0Td5++21WrFjB4YcfzmmnndapogM/Rlr87TaWxFZQUEAoFOLWW2/l/vvvRwjB5MmTcRyHbdu2Nbq90aNH8+yzzzJ06FC2bdvG3XffzXHHHcfKlSuJRBpOqHvggQf2ynvJFEJ4d/+SlP5QSqoHbbpQEmHISs2b0Zz3Jzs7m3nz5tV7z2OPPcaRRx7Jxo0bmzQ4a3FdF0kLo2SlLuIUi8UYOXIkl112Geeeu3d+RKbi3rF4jIDe+iTDtvD2Y+9SuTOXIYc7jP31ZS1+v21ZWGYCy0xi1eTmWKaBbRhYlolteiWltmVhmzaWZWNbDrbpsGG5Q7KiO1/M+y6jBkss9j0AWZG9u/y2lh07dvDLX/6Sbdu2kZ2dzYgRI5g7dy6nnHIKADfeeCPJZJJJkyZRVlbGyJEjmTdvXssbUraCaJUn1pKVl5rqrlW8AymQk7Fw0Jq5c/lw/nxkqUaPRJYRQjDqiCPxh8MMOv44lN1aScTKvf5bkV6tC5G+9NJLvPDCC8yaNYvhw4ezYsUKbrzxRgoLC7nkkkvq1jvxxBNZsWIFJSUlPP3000yYMIFPP/20w70YVVVVzJo1i5KSEs4++2xGjRrVoeP5v0KLDZbGktgOPPBAXn75Za6++mr+/ve/I8syF154IYceemiTB9nu7tcRI0YwevRo+vfvz0svvcTllzdcOTJlyhRuuummutdVVVUtbiSXKq7IXNJtqgdtOrDiUWTHj5Sdmqx/a7w/lZWVSJJETk5OSvtwdpQjKTpqfuqJpOPGjWvSZZ+puHfCMumZ2/53dotf/ZgN3/go6FvJ2F+f06pteGJXGoFQy/JfPv7P6yQrvJuGquIIKxa+h+bT0fx+rwTXH0T3B/H5Q/iCYTRdb3WyuJHcBkjoem6r3t8QzzzzTLPrTJ48uZ4OS3sRj27FdVSy8lK78LqlJRBO33ezJ6s/X0qxptHHcbBcl82yjK1pFK3w2ipo/53PAMkTVlMVhdJYzAsvtrJS6JZbbmHy5MlccIGnD3XwwQfzww8/8MADD9Q794VCIQYPHszgwYM56qijGDJkCM888wxTpkxp+4duJaZp8r//+78kk0l+/etfd9p0hh8jLb4KN5bE9uSTTzJ27FiKioooKSlBVVVycnLo2bMngwalppUAkJOTw/7778/atWsbXcfn89ULPWUS4XoeiUzksKR60KYDo9Qr4dVzUrtotdT7k0wmufXWW7nwwgvrcpiaHdMGT2RN65WZE3E6495VkqBfuH31T1Yv+Y5lc6MEw1WcPzn1apt0kYzt0u+Jl/bg41nghZRqpf8q6r9BcpFVA1m1UDS7ZnJRdRdFE6g6aD4Z1Sej+xQ0v4rm05Bkg+KdNsFuXTf2v37VJ6xZ/WckfEgEkaWgpzGj1E5hNC2MqoXRtAhVVd/gkIeqphZmFOUlkJ25cFDCSJJl21w6dWrd2zo6WwAAYTJJREFUvGhxMUZVFe/MnEnctqkEHFNgSxKOJNGWW8R4PL7XjayiKHXVW41Rm4PUkbz66quUl5dz5ZVXdrin5/8abb4KN/QDqo0xzp8/nx07dnDmmamV+QFEo1GKioq4+OKL2zq0tCBqPSwZMFhae9C2hmRZKQB6bmrGQUu8P5ZlMWHCBIQQdUJqqWBt8dzKWt/0liimO+6dqPIE0EqNeLqG2CzF67fz/rNrUVWTC+86HTnFC1s6OemC8zjpAnBsB9NIYibjmMlYTdl3AjNpYJlJ7zFpYhomlmFjGQ6W4WKbDrYJtgmWIZGsVnAsBcdScSwd19YRjo6niXwl2f2/hpaJ/3YaNm14FzmwBpEcgaAEW0qAnECSvEmWTa9zqQMk8T5yVerhL7m6DJGo5subpiIHAsihIEoohBIKokaCKOEQWiSElhVGyw6jZYXQs4KoKba/SNg2/j0ELcM9ehDu0YNf3HtvyuNMlfHjx3PffffRr18/hg8fzvLly3n44Ye57DIv5BmLxbjvvvs488wz6dWrFyUlJTz++ONs2bKF888/P+3jSZW33nqLVatWceaZZ+4zVjqAFl2Fm0piA0/d84ADDqCgoIDFixdzww03MGnSpHpy1yeddBLnnHMO1157LQA333wz48ePp3///mzdupU777wTRVFa1BQuk+zKYUn/BaO5gzaduEnP8IquLyK73/Bm10/V+1NrrPzwww/Mnz8/Ze8KgL2jCiFC6P3T61JNd9w7ttmTIh92UOZ64uxOvDLGqw99Amice8sxBCIdq72iqAoBNUQgFALSa1w6tolpxHjh7v8SCGU3/4ZOimVtwxX9OPXM2Q0ud10b24phGNUYiUrMZDVZualrdIj9hqOsXo70/qtIdhLZtRB4MoiN6VgLJFxFx1F0hKLjaj6E6kPoPtB84POzpXsO0R4R1vt8DLTarwv8o48+yh133MFvf/tbduzYQWFhIVdddRV//OMfAe/GbdWqVcycOZOSkhLy8/M54ogjWLRoEcOHN3/+SjeO4/D555/z+eefM2DAAA499NB2H8M+WmiwNJfEtnr1aqZMmUJZWRkDBgzgtttuY9Kk+i28a0NGtWzevJkLL7yQ0tJSCgoKOPbYY1myZEmnEQbaVSWU/qTb5g7adBLq0weTjajh1AyKVLw/tcbK999/z3//+1/y81vmsrYrEgjTRQmkN7yX7rh3dJOny1EweP90DrNBHNvhX/e8jWNHGHvZALoP+HHHxxVVJ6DquA4EIp1LI6gl2GIHsmjcIJZlFd2Xje7LJpKVWqLt7hz2r7/Xe+3aNnZ1HKMyilUZxaqKYVfXTNEYTjSOE08gxRNI8TgimURKJiCRAMMAIwlmgrU53TEkl0jSalHovq1EIhGmTZvGtGnTGlzu9/t59dXU+qVlgu3bt/Pll19SWlpKeXk55eXl2LbNkUce2S5l7/tomBYZLM0lsU2dOpWpu8VAG6JWJbeW2bMbviPpLNQm3coZSLpt7qBNJ8lSL1/E3y01Q7A5749lWfzP//wPX3zxBW+++SaO49Q1/svLy0PXm2/w5Vbb4GZeg6Wtce/qrZ6HJWvAgDSNqHFeeeANEtFcDjstyP6jh6VtuwMGDOCHH37Ya/5vf/tbHn/8cZLJJL/73e+YPXs2hmFw6qmn8o9//KNJlep04boudjJIKDv1arFOh7wTTdq7cWK6mTp1KlOmTOGGG25g2rRp6LltExJ8+86pDCjcn4uvav8cqc6IEIIFCxbw4YcfEg6H6dWrFwMHDuTQQw9lwIAB9GplVdQ+0sO+ovFmELXS/FLX7rBplJUDIQIFqd3dNef92bJlC2+88QbAXiV9//3vf/dqBtgQriEhKS1zQ0ej0XoJ2evXr2fFihXk5eWRn5+fkbh3tLgYSQgi/fq3ehupMPfp9yjZks3Ag5McfU7jukWt4fPPP8dxdhmHK1eu5JRTTqn7XiZNmsRbb73Fyy+/THZ2Ntdeey3nnnsuH3/8cVrH0RCJaAXC0emqESHXdZB9pfik1AUTW8Pnn3/Ok08+yYgR6Sn9FkLgYhEMBNKyva6OEIIPPviAjz76iBNPPJFjjz0WRdmnVtuZ2GewNIPrmgDIctd1VwNY5dWggR5M7arQnPdnwIABzTb+axZXRw62zGBZunQpJ554Yt3r2vL2Sy65hCeeeCIjce9oWQk+VyCn4DVqLUvf/pzvl8rkdi9n3G9bV77cFHuGWKdOncp+++3HT37yEyorK3nmmWeYNWsWP/3pT4Fd+WhLlizhqKOOSvt4dqeqzAsRh1OsYOtsRCt3ICs2oWDLQz0p7yMaZeLEiTz99NPcm6YkWCNhISSHYCiYlu2lGyEEa9euZcmSJUSjUYLBIEOGDOGwww5Le5WoEIL33nuPxYsXc+qpp3L00Uendfv7SA/7DJZmqNNhyUAOS3viVBpIgU52t6CEkCNmi95ywgknNGkoZSLuHa+qxC9lrn/LuhVFLHm9BF8gys/vOCfjvWJM0+T555/npptuQpIkli1bhmVZ9dSohw0bRr9+/Vi8eHE7GCxlAERSrGBrDZ/PnU759s1oPj+aP4jPH0QPhND9IfRABF8ggi+QjR7IIhDKRtVDKf8fyks2eOPPyVxTzGuuuYYzzjiDk08+OW0GS0VZNQDhrM7XULOqqop///vf/PDDD/Tu3Zu+fftSVVXF+++/z0cffcTpp5/OQQcdlLb9fffddyxevJhx48Z1WDfofTTPPoOlGYRrIUmtF8TqLIhqINQy4yCT2FVxJNWPmtf5TpZ7Eo9F8beyZ0pzlG0t4Z0nvkGWHS7441hUPfOH5GuvvUZFRQW/+tWvAC/BUNf1vQT/evToUZeXlEmiFVWASk6K+VUtxbFNFj37HyQZBCDsFAwRSaBoAlkDVcfry+RTUHUV1aeh+TQ0vw/N7yee2IiWEyJ3xICMjH/27Nl88cUXfP7552ndblWNwRLpZAbLli1b6nIbJ06cyODBg+vOvxUVFbz33nu88sorCCE4+OCDU96u4zh89dVXbNq0idLSUrZt24amaXTv3p2qqiqysrL2GSudnH0GSzO4rtnlw0EAUkxH6p5+fZfWYtaKxvXsvIkLQgi+/c9/2NCjL6Y/wD133F5v+Z4mrPda1D2T6j3ftVLdcwHB8kNR8HH2pEOI5LXPd/HMM88wbty4FjWpzCTRyjiS4ieQopLr2i/n8sW7L1L2QzVWwmbYTw/wVHcDIXyBEHogjC+QhS+YhS+QjZGoQLgyJ/z6TEadeAmWFcdMVGIkqrwpXo2RjGIlojVaMzVTIo6VTHjtDBIGlmFiGRbJaJJYaQzbdHFMgRmVkeS+nHlx+g2uTZs2ccMNNzBv3jz8rVSVbYyqyhgAWTnhtG63tTiOwxdffMHcuXPp0aMHF1xwwV7tWXJycjj//POZNWsWH3/8cUoGS1lZGStXrmTFihWUlZXRs2dP8vLyOO644+pax+Tm5nLsscdm6qPtI03sM1iawRUWsty1E24B5HgIKSv1JoOZxtrk5S3ofTq+mVtDFH3wAR/MncvWYJAcRaV3bg5SwA/CM2SE98S7Yxd7PK953P35rnkA3rJEQkW1c+h9iEvv/TMXTtidH374gffff79e6Kxnz56YpklFRUU9L0txcXG7yI5vX7WaZPkynrjmUdRaBVy/jubX0QM+dH8APeBHD3ohnNUff0z5RgO3xlPyzXvLcSwZRNNe0F6DD0GWfTVK2blEctIz/n/98a8Ur/sSRUl/KG/ZsmXs2LGjnu6H4zgsXLiQxx57DMMwWp0YWl0VBSA7r30VnPckmUzy7bffsmjRIsrLyzn00EMZN24cmtbwjaIkSRx22GHMnj2b4uLiRivZduzYwfz581m1ahWapjFs2DDOP//8fZU+XZh9BkszCNfMiMpte2IbMVQzCyk3s7kRLcEqrgAC6IM6x11+LVuWLGHenDlsCATIlmXOHD6cUeedl5G8kucfmksFDsedfUTat90YM2bMoHv37nVNLcFrsaFpGh988AHnnXce4Gkqbdy4sV2SD+OlCSQJeg3rhZlIYCYMzGSCeGUUO+nimGAb4Fq7/ge9Ds7motv/VffacSwso4pkvJxkvAIjXoURr8RIRDES1fj8YXr1z8z3HKssRfNnxjt20kkn8fXXX9ebd+mllzJs2DBuvfXWNlWxxKo95easnI4JCTmOw8cff8zChQuxbZthw4ZxwQUXpFRKP3jwYILBIMuWLeP00+vLI5eWlvLf//6XlStXkpOTw1lnncXw4cNTklrYR+ema1+J2wFX2F3eYEmUep1h26rZkE7s0jiuYaFkd44xFX/6KXMfe4z1AwcSlGVOHTSIIy+6CCWD7eJd10VCA9E+hqTrusyYMYNLLrkEdbfPlZ2dzeWXX85NN91EXl4eWVlZXHfddRx99NEZT7gFMGIxAll9OOeGxhtrAriOg2XGSMbKCGXXv0tWFA0lmI8/6HnsHMfhrrvu4vnnn2f79u0UFhbyq1/9wO23316XD/Hqq6/yxBNPsGzZMsrKyli+fHmruu4moxUEIplpjBmJRPZKLg2FQuTn57c56TQWiyEJpVFPRiaxLItXXnmFNWvWcMghh3D88cen3DQVQFVVjj76aD744AMMwyASiSCEoLi4mHXr1hEOhznjjDM45JBD6v3W99G12fefbAYhnC5vsBglOwHw53cO9WAAp8oEx+zwZOaqrVv54Jln+No00Xr3ZrRpctJdd7W6C22qWJZN1QYZNSdJTkH75BC8//77bNy4scHWD4888giyLHPeeefVE45rD8xEJVkp6APJiuLlpgSaN3IffPBBpk+fzsyZMxk+fDhLly7l0ksvJTs7m+uvvx7wLtjHHnssEyZM4Iorrmj1+K1kBd36Zl40Lt3E4zFUqX2ayO7JW2+9xbp167jwwgvZf//WKUgfc8wxAHz99df88MMPSJJEbm4up5xyCocffniHGGL7yCxd+0rcDmzZ8i9su6Kjh9EmjLIKIIK/oPPEbkWCFovGpZN4ZSUL//lPlpWUICSJw7Kz+ekVVxBowV1eW1ixeBWSq3HQ8T0zkvvQEGPHjm20JNzv9/P444/z+OOPt8tYdscxqwlm56V1m5988glnnXVWXehrwIAB/Otf/+Kzzz6rW6e2weqe6tstwbFtXDtKpA3NNVvKhx9+mJbtJJIJhCOx5uv19B/cG1+g/UImGzZs4PDDD2+1sQIgyzLHHXccxx13XBpHto/OzD6DpVnSV1mzctt/mb3yCWJ2lFxfLgXBHvQK9yagBvApPvyKj6CeTVagD/nhgUR8XlxcCAdXgCK3Ll5tV8RBVdGCHZtctzvCUZED7W+w2Nb/b+/Ow6uozgeOf2fm7ktudpKQQNghQIIiIouIiiwi4m5d6lq1danFWoW6oYjQX23FakVt3aq11qUotSqCioqCssi+hyWEhJD97tvM/P4IiUQC2W4W4Hye5z6Qe2fOnMkkue89c877Rnj/+RfYWF4z6Xew0ci4X/wCV2bbJf1qsB+hmp8rm7NjPuF2FmpURVN9OBJjG7CMHDmSF198ke3bt9O3b1/WrVvHsmXL+POfj33bqbnK95cBGvFdjr/KvbquEVV8vPnea3Rx9uBXv72+8Z1iQNM0vF7vESuABKExImBpxEptIP/YtxZeO/J+sQzIUv3lrYd/ftX1+l+rSNhlSDaZ8ET3UR3VUI9YHHskk6QT1SXSzCbSLHbQNTQULAYL8SYnLnM88eZ4XGYnTpMTlzmFeFsG6XF9SbFnoLnDYPW28DvQRmQ7Slzb1xGqpes6P3ywkKXfrcB9KEvmzydOotcZHZN3YcDQHqx5r4zyA9UdcvzOorK4HNBwpcR2hGL69Om43W769++Poiioqsrs2bO55pprYnqc0oJiAJIy277mUqzdeMfV7NleyDvvv0mJZzd/nj2f0aNHcvpZeW163NpCgi2tni6cvETA0gi30pUUyx6uzK6/WkJHR9MPPdCQkA4LPSRqp2Yc/rxBNnHJwLtItGcwf/58nnvuOfbs3Q069OrXg5vuvoZTzuyHN1DM/oq9zHrsdfZ/cxBFkxlweiY9b03Fo4eQUZBRcYcq2O87gDeq4tMg3MCyThl4eP8vGO45lRWz/kNQCRGSI6hGHYyAWUY2G5BNBmxJTkaMO6/N62dEq71IRhtKUvsELDu++YZP//cRpSYjXcMRLjnvPLIPpaDvKPGHlpLu/LaKcy/RkNvptlBnU15YAkBCWmwDlrfffpt//vOfvPnmmwwcOJC1a9fym9/8hoyMDK6/PnYjCd6KKgD+N+9B/ve0EVk2Y7Incdv8pzr9ZE+b3ULOKb05Y/dYdu7cSamvkM0btrV5wFJcXBPkieXFQnN17t+ozkA2k+bozq3D/xjTZjMzM/nDH/5Anz590HWd1157jXtvfpgffviBMwdezK9+9Sv0LQY+XfhpXTG69X8NHrUYnaZF8Yer8YQqqQoUUeEvpMRbQIl3P5XmQjaVO1EMNoxRI86oBSkMildCqZIwRg0khmsmMm6I+44hI0bG9Fx/KpRfs2rJmNZ2qdgB9m/ZwqK33qJAkkgMBbksdzADr7zyiIm+8+fPZ/78+XVzGQYOHMjDDz9cV0Z+7NixfPnll/X2ue2223j++edb3rddNROhNV09MgPdSaTyQM33ISkztvlefve73zF9+nR+9rOfATB48GD27t3LnDlzYhqwDDlvJAGPF3+1m5A/wIH8zfgrd4B2/FSennDJGCYwhsceno3F2rLJ5l6vl9LSUvx+P4pSs/LI5XJhMpmorq7GbreTkJCAJElUVFRgtVqx2ztXhl2h8xMBSyNUXUVugzoyU6ZMqff17NmzmT9/PitWrCAzM7PZxehk2YDDkoTDkkS6q3f9F5sQf+zYshHra5Ukf6Dy+Yq30EygmyUks4JiMSBbjBitJkw2CxabDYvNit3pxG53YrM5mvVpMrKv5k3KlN02Q8KVxcUsevVVtgWD2AMBJnTvzukzZqAcpWBaZmYmc+fOrRc8Tp06lR9++KGuaOItt9zCY489VrePzdbygnEVpdUs+L91SMgMPLtLm9cO6syqD9b8LCRmxHaExe/3H/F9VRQFTYtttmezzczYa3/8XX7n8WfZ5y7CYDr+VqhoUpRtBeuY9chmZGQkSUaWFOTaf2UZRVZQFAPnjDuLnFN74/f7+eijj9i4cWOj7aekpNCjRw+KiopEsCK0iAhYGqHpWpsELIdTVZV33nkHn8/HiBEj2r0Y3RcFX/DwuocZmTaY0cFTiYs4MftlTFED5qgJi2rCph3+ycsP+FEpxw1UoRGQQwSVMGFDhLAhSsSoopp0NBNgkpEsCrJFwWAxkbzRg1Prwta93+Cq2osrKR2zKw5zQgJGq7VVS51Ld+7k+ddewxCNMsrhZMzdd2NqZELnsYLH2oDFZrPFLOur2WICWQNNpmhXZd3z0UgUg/Hk+pX0lFcgyTaM5tiuUJkyZQqzZ8+mW7duDBw4kB9++IE///nP9ZZ0V1RUUFBQQFFRzYjftm3bgJrMvy291r6qCoymzpFbqLlGDx1HaUkpUVVFjapE1SiqqqJpKqqqomo1/68M7mP9D5vpl9eDN998k7KyMiZPnkyPHj2w2+1omlaXOTkYDBIfH091dTUbN24kPz+fQCDAhAkTOvp0hePQyfXXsYXaKlfIhg0bGDFiBMFgEIfDwYIFC8jJyWHt2rXtUoxuwY4F/OH7P+CL+pAlmaRR2UweelODn/hVVcXv8+L1ugn4fPh9PkL+AGF/kGggTDQYRgtG0UMaUkhHCoMxrGDwyZgiRsyqEYtqwqqZgTjUql1Y751LGCg97DiaJBE1GlFNRjSjCc1sRjeb0S0WsFpwu+KpSEkmLi4Ok9lMUkYGp1x+ec2+msZ7f/sbusnELT/7Gcl5zb8X/9PgsdY///lP3njjDdLS0pgyZQoPPfRQi0dZ7E4r1z0xitce+JrKbSa++ngV6xeVQNDMubf2YsCpPVrU7vHIV12BwRT71SLPPPMMDz30ELfffjsHDx4kIyOD2267jYcffrhum4ULF3LjjTfWfV17++iRRx5h5syZLTpu0FuFydZ562Mdy7gLRzW6TTSs8vjsWdgdNoqLiyksLOTaa6+ld+/6o7q1t4Bqpaen079//5j3WTi5iIClA/Xr14+1a9dSXV3Nu+++y/XXX3/EXIm28mH+hzy6/FEArux7JdOGTsNuOvowraIoOONcOONa98dYjar4vG48B+N4tHcimWoyt/W5jojHTdTjRfV5Ub0+NL8PzeeHQAA9EIBgEKm8gr3OOIplGXNlJWGDAbWykoGTJ2Oy2fjoiTkcsFo5v2/fZgcrRwseAa6++mq6d+9ORkYG69ev5/7772fbtm316vE0V1y8nT6j48lfXsWGD9xIWAGoLPW0uM3jUdBThcka+zd4p9PJvHnzmDdv3lG3ueGGG+oqVsdKOFBNfFqvmLbZmVSUukGCOJdTpLoX2p0IWDqQyWSq+2QydOhQVq5cydNPP82VV17ZZsXoomqUaz6+hs3lmwH47dDfcsOgG1rVZnMoBoW4+AScrnh2pATpmTSQPhde2uT9V8ybhzEQ4P7HHuOLZ5/lq4OlbPzkE0r37mVVNEK218uwq65qdr+OFjzm5ORw66231m03ePBg0tPTOffcc8nPz6dXr5a/OU286gyil6t88/F6Kg54KFodZd/2MjiJRsvDATfO5K4d3Y2YiYbd2BNim1OmM6korVmGH58YR2JiIgaDgc2bNx8xwiIIbeHkne3XCWmaRigUqleMrlasitGtK11XF6x8cfkXqLrKf3a0fKSgpYqrignLYXom9GzWfpqm1d2yMhiN6LLEwvXrWV5dTWogwGW//W2LbuHVBo9Dhw5lzpw55OXl8fTTTze47fDhNblbdu7c2ezj/JTBoHDWlFPo3r9mAnL/09o3gV1Hi4Y82Fxtu1qsvYT8IXQtQFw7Zr1tb1UVbgD+++l7zJkzh2g0SteuJ07AKXRuYoSlg8yYMYNJkybRrVs3PB4Pb775JkuXLmXRokVtWoxuaNpQTk09lTUH13D2O2fXPV/iK+FXQ37V2tNqsk37NwHQL7V5NVgOD1hG33orefv2oYcjGOw2rF1it+KmNnhsyNq1a4HY5pHYvKIAXdEZNOzEvZ3wU2o4gqb6OLhlCW/e+jEmo4LRqGA0GTGZjBjMZkxmM0aLFaPVhtFiw56cTvcJNyB1gpVV4ZCf/z0/DTUaxmA21VWT9kfWsmaZB6PRgdHkwGS2Y7I4MVscmK1OzFYnRpP1uFsdpus6heu8WNQk+uVlkZ6eRs+ePUUCOKHdiIClEVIbJck4ePAg1113HcXFxbhcLnJzc1m0aBHnnXce0LbF6J4/73nmfj+XqBqlZ3xP5q2Zx8L8he0asGwv3Q7AwK4Dm7Xf4QGLJEnEdevW6r4cK3jMz8/nzTff5PzzzycpKYn169czbdo0xowZQ25ubquPXWt/sYJDlZjzyLdoRgndKIOp5iEbZSSTjMGsYDApmCwGzBYFs1nBajFgtxqxWww4rAacViPxNiNOswGrIiN3cHHJYwmWHsRgG0umaw0WS5hIOEIkEiXo9hOOakSiOpEoRFSJsCajH/pdvCEti6Qh46CDz61g8zJ2fbsfa4KGroMa0THadUxd36UyHIEw4Gt4X12X0KMW1IgZLWpGkW2gW0C3IlHzkCUr2L9C0jIwqWNQDFYUxYpisGI0/viv0WTFaLJjNFsxmu2YzbaaIM9oQJJj9z06uMdDybYgF994Of2GxzZvjiA0hQhYOshLL710zNfbshid1WDl0ZGPElbDjP33WABuH3J7zI9zLHuq9mDRLCQ6mne/X9O0mGcQPVbwuG/fPpYsWcK8efPw+XxkZWVx6aWX8uCDD8bs+NtLy1mU5+D0apUEgxktrEFYRQ9rSF4VKaIhRzSUiI4SrXlEgSg174dlDbSpShA2SESNElGDhGaU0IwyHAp+JJOCcigIwiSzyh8gL8lBZpwZq8WI1aJgtxpwWIw4rEbibAbirDWBUKyCIF9xMQZzHqOuHUXKaacfc1td09j9vxdZ8MaHvDr3aczyn7AaVYyKhKwoNQ9ZxmCQsdvMOOLjcCalktIvj65nXRGT/v5UdVkBAFfP/jPxST+ugNE0DTUSIuj3EAx6CAU9hINeIiEv4bCPaMRLJOIjqvnZt3s/jiQdY5yKrvvR9AC65EWnFFUKoMjV6HI1AXYjSWEkNOoufiO0qBFdM6KrZnTdxKHkSkiY6/6VJDOyZEGWzciyBVm2oiiWmofBgsFgQ5LM7Ftvo2CthYQ0Gz1P6TxV34WTiwhYTlJRNcrZb5+NJ+Lhir5XMKXXlMZ3iqFCXyFJUlKz99N1PeZD6ccKHrOystp85dbCXfvY3M3Ma6f0JCu+8RweuqYTCasEA1E8wSjuQBiPP4ovGMUfiBAIqgSDUUIhlVAwSiSsUl5ygA/fe5qtW74lEg6SlJjJpedPp1tSX+SIxt5Fc3l326f1jjMgcxh3TJ5b7zlVrgmE1EPBUG0QhElGOhQMKYdGgoxmuWY0yKxgsRiwWgw1gZBFoatWRmhbPpCCrQm31iRZJnvSTVxmtuEtLcJfXYWvuppoKIgWCaFFw2hRlWg0SlWlh31F1fjC++GzH/jNqWcjOWP/JusuK0I2aDhc3es9L8systmK0WzFSSO3S847+kszZ87k0Ucfrfdcv359Wb9+JSXF+5k1aw6ff/EVhfsPkJjoYtzY4dxx26VYLQrRaABVCRBVg6hqAE0NomlBND2IroVq/iWARhWaFAI9DHoISQ8jaeGa4Ciq1gVGpm4uxgz4gD7D0jGa2rZ0hyAcjQhYTlIHAwdxh90YZSMPjXio3Y9fGiklw5LR7P3aImDpaN9WekjV5SYFKwCSLGGyGDBZDMQBjU15rKys5JRTLuLss8/m+ecWk5KSwo4dO+jVq1fdKqdNN7xGYvZEnnvuRXyhKJ5AhLCmoJgc+AIR/MEowWCUYFAlHFIJh6JEQipqSEMNqzWjQgEV3BG0iIYa0SGioUd1NBUCQOVhfVoPQAqKFMbapWlzgWSDie7jr2vStgDf/fV+Vi1bi2Rrm0m93soyjHYNg8HaJu1DTZmIJUuW1H1tMBgwmeKo9hRQXunjqXnPkJOTw969e/nlL3+JP/xf3n333ZgcOxqNEA75KC9bxvb8u0nL2YnZmhWTtgWhJUTA0giNo6fyDqthCtwFxFviSbYeXysD3tv+HgDThk5r92Pruk4llQx3NL9SsqZpbV6csb1tRmGooe1qz/zhD38gKyuLV155pe65Hj2OTE5nsZjp2TP2b0hRVcMTiOD21wRCr6z8mu0hE89mOLAmpyO3UZFAf0UZNrMOStu0762sxOxs259Fg8HQYCqDQYMG8d5779V93atXL2bPns21115LNBqNyW1Tg8GIwRCP1TaZA6Uvs2/fKyQnn934joLQRkTA0oijpeb/YOcHPLnqSapCVQD0ju/NdTnXMbX31DZP5R8Le9x7ALii37Hv73/11Vf88Y9/ZPXq1RQXF7NgwQIuuugiACKRCA8++CAfffQRu3btwuVyMW7cOObOnUtGxtFHT4oqi1q0pBlOvBGWrSWlVJltnBnfdrVnFi5cyIQJE7j88sv58ssv6dq1K7fffju33HJLve2WLl1KamoqCQkJnHPOOTz++OMkJTX/tt1PGRSZBIeZBEdNLSfvFj8mW5juZ7dtwpmAx407qPDmLZOwmhWMZiNGkwmj2YzRbMVktWG0WDAcWoVksthJGXQGcb1OaVr71T6scS0rFthUO3bsICMjA4vFwogRI5gzZw7djjLRvLq6mri4uJjP8ZIkiZTk88jf9SQ+307sdpFzRegYImBpjH7kSqGXN77MU6ufYkrPKVzS5xIO+g+ypGAJD3/7MN8f+J7HRz2OInfuUYACdwEG2YBZabggYC2fz0deXh433XQTl1xySb3X/H4/a9as4aGHHiIvL4/KykruvvtuLrzwQlatWnXUNuuWNHdp3pJmqAlYTqQRlv/tKQQkLujV+tVOR7Nr1y7mz5/PPffcw+9//3tWrlzJr3/9a0wmU13l4okTJ3LJJZfQo0cP8vPz+f3vf8+kSZNYvnx5zL/f+3UzPeSGl4zH0ikXXYv98w8I+gMEAiECwShuj4dIpJpwVCeiQUSVieo/nl+6679c/eJHTWo/6AkTH+OijYcbPnw4r776Kv369aO4uJhHH32UM888k40bN+J01i9nUFZWxqxZs+olOYwlp7NmNV8gWCgCFqHDiIClETp6XSIyXdd5aeNLPL3maW7LvY07T7mzbrvze57Px7s/ZvrX05GQmDVqVsyDFl3XWbx3MR/u+hB/xM+ApAFcM+Aa0uzNX2JY4i8hrglF2iZNmsSkSZMafM3lcrF48eJ6zz377LOcfvrpFBQUHPWT4I7SHQAMzGjekuZaJ9IIy7JKD6kYyIyLfT2dWpqmcdppp/HEE08AcMopp7Bx40aef/75uoClto4O1GTzzc3NpVevXixdupRzzz03pv0pkp2MNoZj2mZD0kdOJX3k1GNvpOvokRARfzX//f0NSM24fRTyaG2a1fbw37vc3FyGDx9O9+7defvtt7n55pvrXnO73UyePJmcnJwW10BqjM2WDYAsHX9VqIUTx4nzl7+NaLrGd8Xfce+X9zLunXE8veZpfpn3S+4YcscR207qMYk/nPkH/rf7f8xaMQtdj928BFVTefjbh/ntl7+lKlRFnDmOD3Z+wKULL2VbxbZmt+cOu0m3xy7xWa3q6mokSTqicOPhdlftxqpZSbA3fzLkiTTCous6mzGS18YfG9LT0+vqItUaMGAABQUFR92nZ8+eJCcnxySb7+Gi0QgHjPF0tRx7ZK/W/Pnzyc3NJS4ujri4OEaMGMHHH39c93p+fj4XX3wxKSkpxMXFccUVV1BSUtL0DkkSksmCKb4L/hA44xxN2i3oc6NFZJxJ7Zc0LT4+nr59+9a7Jh6Ph4kTJ+J0OlmwYAFGY9sEFAZDTb2nSNTdJu0LQlOIEZZGOA9Vki1wFzC512TO7Homw9KGHXX7iT0mElJDPPjNgxR6Cjm1y6mk2FIYmzmWFFvjSys/2f0JnxV8RpI1iQt6XkDv+N7sde/lhfUv8HnB5zwx+om6JcjVoWpu+fQW7v7ibv578X8xyk37Y+UOu1F1lZ7xzZ9DcizBYJD777+fq666iri4o4/e7PfvJ1Fu2SfTEylg2VhSSrXZypiEti0iN2rUKLZtqx/Ubt++ne7dux9lDygsLKS8vDym2XwBSir2o0kKGY74Jm2fmZnJ3Llz6dOnD7qu89prrzF16lR++OEHsrOzGT9+PHl5eXz++ecAPPTQQ0yZMoUVK1Y0eyTOHdTpk9i0OTtVpfkAxCW1X1p6r9dLfn4+P//5z4GakZUJEyZgNptZuHAhFkvbzafxemt+fqwWkYZf6DgiYGnEjOEzuH3I7aTamv5JamrvqdiNdv655Z+8s/0dKoIV/MnwJ27Pu52rB1yNQT7y2+6P+Jn93WwW5i8kNzmXNQfX8M8t/6x7Pd4czx/G/IEJ2T9OVHSZXdyWexu/WfobygPlTb419F3xdwDkJscuU2skEuGKK65A13Xmz59/zG0Phg+S1YrlkbGeVNhR/re7EPS2nb8CMG3aNEaOHMkTTzzBFVdcwffff8+LL77Iiy++CNS8ET766KNceumlpKWlkZ+fz3333Ufv3r2ZMCG2E2P3V5YAZjJcTQsMpkypnx9o9uzZzJ8/nxUrVrB//3727NnDDz/8UBcgv/baayQkJPD5558zbty4Jvcr4nMTjBqIS+3SpO0rD9YELAmpR662ipV7772XKVOm0L17d4qKinjkkUdQFIWrrroKt9vN+PHj8fv9vPHGG7jdbtzumtGPlJSUFgf1kYibaNSNxZKBdGjxQGXVSrZsnY7F0rVuLosgdIQT4y9/GzIr5mYFK7XGdR/HuO41fzCrQ9U8+8Oz/Gn1n/hw14dc2e9KspxZhNQQG8o2sL50PevL1hPVoswePZsLe12Iqql8V/wd5cGaQCQ3JbfBCbIuc81QrTvsbnLAsubgGgBGpLeukGKt2mBl7969fP7558ccXdF1nSqqGOFs+bFPlIBlebWPNF0m3dm02xAtNWzYMBYsWMCMGTN47LHH6NGjB/PmzeOaa64BQFEU1q9fz2uvvUZVVRUZGRmMHz+eWbNmYTY37dZNUxV5KoE0MpOaX+RRVVXeeecdfD4fI0aMID8/H0mS6vXRYrEgyzLLli1rVsDi2VtTENSZdvRRp8NVlxUCEJ/adhNQCwsLueqqqygvLyclJYXRo0ezYsUKUlJSWLp0Kd99V/PB46eVknfv3k12dnazj1dauoT1G34FaJjNacTFDSEY3IfHswmnYyCDB/8VSToxRjeF49OJ8Ze/k3OZXTxwxgNc2OtCnlrzFDOXz6x7Ld4cz+Dkwfw85+dMyp5EtisbAEVWGNl1ZKNtpztqhuwP+g/SN6Fvk/qzrWIbEhLdXU3743wstcHKjh07+OKLLxpdBru/fD9hJUyvhOYX+dO0mpw4J8ItIU3T2CwZGWk4ep6fWLrgggu44IILGnzNarWyaNGidunHfp8PZ9SHs4m3hAA2bNjAiBEjCAaDOBwOFixYQE5ODikpKdjtdu6//36eeOIJdF1n+vTpqKpKcXFxs/rl2V8zL8SZ2adp25eXIJs0bI6mjci0xFtvvXXU18aOHRvTOXIAXu9WQCMv92+UVyzD79uJzdaLHtl3kpw8rm7ERRA6ighY2tHglMG8POFlvGEvFcEKJEki05FZtwqpJWonzv5qya9Yfe1qTErj8yEKPYXYjfYmte/1eutN8tu9ezdr164lMTGR9PR0LrvsMtasWcOHH36IqqocOHAAgMTEREymI/uyqajlS5ojkQhw/I6w6LpOcVkV2wuKWb2/GI8ziS3RKLcv24bdoBx6yDgNCk6Tgcndk4g3n1irMvaHImSo1c3ap1+/fqxdu5bq6mreffddrr/+er788ktycnJ45513+NWvfsVf/vIXZFnmqquu4tRTT23+/JXivQA4ug1o0vbeijLMdlr1u9vZWKyZgITJlEK/vg93dHcE4QjH51/+45zD5MBhis1tAFmSybBnUOQrYugbQ7mg5wU4TU4SzAkkWBJItaUyOHlwvQm/FcGKJq8QWrVqFWef/WN2y3vuuQeA66+/npkzZ7Jw4UIAhgwZUm+/L774grFjxx7R3raDNZP3BnUd1JzTBGqCJ6gZEeisAuEoX2zYy7aCYgxhHxWVlfg9VWhBL2bVj+FQ5uRqiw3rkDEUGM0UhPwQOfKNb+bOItafl4fVePyPKNUqUmW64m/WPiaTqe62x9ChQ1m5ciVPP/00L7zwAuPHjyc/P5+ysjIMBgPx8fGkpaXRs2fzJpR7Sg9gN0Qw2OObtL2/yoM57sQKJrukTqag4O9s3vI78nJfwmoVE2yFzqVZAcv8+fOZP38+e/bsAWrqXDz88MN1+QLy8/O59957WbZsGaFQiIkTJ/LMM8/Qpcuxh03/+te/8sc//pEDBw6Ql5fHM888w+mnH7t6q/CjRZctYnP5Zt7b/h47qnbgjXipClZRGaokqtVUL+vq6MrknpO5uv/VhNQQ2XHZTWq7saHn5g5L767ejU2zEWdtWt2cw1VUVAAcc45MewiGo2zee4AdBQfYX1JKRUUFAW81hLxYtQAGqeZ7oukQki3oJjsWVwquhAS6JCfTLSOVft3S+HO8HUmS0HUdf0SjKhihOhKlOqxy06qdVDoU+n/6A4/1yuD6/s2vu9QZFWIFo61VbWiaRihUP/FccnJNArfPP/+cgwcPcuGFFzarzarSQnxRI/+dfwdmmx2z3YHFHofFHo/VEY/VkYQtLgW7MxWTNZ6A248toWmjlG1B06Koqh+DwRGzWzWybGRgzp9Zu+4mvvt+Ij173kNW5nVi3orQaTQrYGmLJYb//ve/ueeee3j++ecZPnw48+bNY8KECWzbto3U1PbLcXC8y0nKIWdE/Vwbuq5z0H+Q9WXr+a74O17f/Dovrq9ZGRJvie+AXtYsaU6Wm58dNDs7m717a4btD69ge+mll3LfffchSVK94Xld1+sFU4f/v3Y7SZLQdJ3KkIzNGYfJZMBsNGA2GjEZFIJBP3v3H2RfSRmlZRV4PdXoQS9mLYhSF5RIRBULZosTW5euJCUmYnPG0zurC8P6ZmEyNf4pXJIk7CYFu0mpK2S4+YJTeXZTIU8VHOT+4oO8vb+C507vRXdn5x1daortpjTOk8ubvP2MGTOYNGkS3bp1w+Px8Oabb7J06dK6OTevvPIKAwYMICUlheXLl3P33Xczbdo0+vVr3i3HJFMh8dY49qzejRoCNSwBR7ndI+mgQ3xW29V/aoiu6xQUvMi+wn8QCtXcerVYsujb5wFSUo5R9rkZHI6+nDH8Y/J3/YkdO2ZTUvJfBg9+Dou5+ckpBSHWJL2VM7cSExP54x//SFZWFpMmTaKysrLuE3B1dTUJCQl8+umnR52xP3z4cIYNG8azzz4L1Hx6ysrK4q677mL69OlN6oPb7cblctXV0hAaVhGs4MFlD/L1/q/JdGSyYOoCLIa2rYXyU2NfHktve2/+fuXfm7VfaWkp69at45NPPgHg4MGDvP7661x//fUtWhHRHBFdJihbkS127E4XKclJdM/oQr/u6fTMaPkS0qYIRlXGfbqBclQe2hwi3m7CkG4nvruLQf1TsFmPo9sSmsqITz5mQpyRmaObtlz65ptv5rPPPqO4uBiXy0Vubi73338/551X8wY9ffp0Xn31VSoqKsjOzuaXv/wl06ZNa/7ckpkuPJm9cP5izaGuRgn4yvF7Sgl4yvB7ywl6qwh4qwn53RQV/o/kvjopXXNQFBuyYsdgcNQ8FAcGgwujMQ6DMR6DwYXJmIDRmICi2Fo876W09FPWb/gVGelX4IofiiybOXDgA8rLl5KX+yLJyee0qN2jqapezYYNd+J05jAk76WYti0ItZrz/t3iOSyxWGIYDodZvXo1M2bMqHtOlmXGjRvH8uXLj3rsUChUb0i4Nv+AcGyJlkSeG/ccy4uWc9fnd/Hkqid58IwH2+34UTVKlVRFN2fz846kpKQwbtw4hg4dit/v56GHHqJbt278/ve/P2I0BepPhvzp6F7ttrqu8+9FX+OvKGHyxZcTikSJRlTC0SjhaBTZaKF7egr9uibhsHRMYGAxKCw7fwj/W7yDvLIDqOUBlL0BWFHG6rg9DP3t6djMx8dUNM1byn5zKpnWQJP3eemlY79Rzp07l7lz57a2awDY9+fX/V9WDNjjumCPa/h29ldf/49IxE1F5bdASz7zyUiSjCQphx5GZNmIJJmQZRMGg4OBOU9ht/84F6dg3yu4XKcxYMCcuue6pE5m3bqb2bFzDklJZ8d0EnC8ayjdu/2CHTufQFX9KErrbuUJQms1+y9dLJcYlpWVoarqEXNcunTpwtatW4/ahzlz5tS7LSA0z4iMEUwbOo2538/lupzr6BbXtonLau0u3Y0qq/RJatrS0YYkJCRgt9tZuHAh99xzD337Nm0p99GEPv4G1WDjzLzWtdPWMg7dCope25+kbBffv/gDfUvC7J21nP1npDLuguavumpvpdUHCMsmujk615wINeJDAcoGDqOpN6HHnLmy3teaFiUSdRONVBKJVBKJVBOJVBGNeoiq1ahRH9Gol6jqQ1X9qKofTQ2gakE0LXToEUbTvEQiYQKBIAdK3qdXz3uIRKrYW/A3qqq+Z/Cgv9Y7riTJZGZex7r1v2DVqkuIqn6iUTc2Ww+yMq8jNXVii78vXu82du1+mjhnrghWhE6h2QFLeywxbMyMGTPqVqtAzQhLVlbLM6eejKb2msr/rfw/VpWsareApXZJ84C0pi0dPZr333+fqqoqbrjhhlb3KRL0o5g7/7wQ2XioAGdUw2E3MfbuYWxeXUzll/vov+wg5SOySErq3G8q+6orgQQy49quwnFLhCs2YAWsPRvOU9MUsmzAbErEbGp9McSSgx+zceOd6FqUrdse4sCB99F1jR7Zd5GScuSttKSkMWRl3UQodACzKRWDwUlV1Uo2bLyDAf3nkpFxeYv6UVT0NgaDk1NO+UdrT0kQYqLZAUsslxgmJyejKMoRxcpKSkpISzv6JC+z2RzzDJwnG4fJQXZcNlvKt0DLBzyaZXvZdiRdon9G/1a189JLLzFp0iQyMlq/ckYPBzEntl3yr1iRlJqgX4vWLIuWZZlBw7ry0e5KKKskHOMkYm2h0OsBEshM6FyT6cNlm7AChsScRrdtD4FAzeTyvQUvYDankZV1E1mZ12EyNZyUUZIU+vZ5oN5zuq6zbdtDbN32IBZLBomJo5rVB13Xqaz6nri4XAyGtqskLgjN0eqb361ZYmgymRg6dCifffYZF110UV17n332GXfeeWdruyY04tQup/Lp3k/5zdDfNDmRXGvsqd5DnBaHxdjyib579+5lyZIl/Oc//4lJnwxaELujbVPjx4JiqLmNoqr1M+PK0ZpApfTZtaztH4cuS8iKBLIMioQkS0iKhCxLSIpc839FRj70XM2/NV8rsoQppGKsjqBnOZBNMgaDjMGgYDYbMBokrGYDFpPSolHT/cEQzqiPOEvnGtFSK7cDYE7J6+Ce1PB6anIVJSefx+BBzyA3sajp4SRJom/fmQQC+9i8+XecccaiZgUe4XApXu9mune7pdnHFoS20qyAJRZLDM8991wuvvjiuoDknnvu4frrr+e0007j9NNPZ968efh8Pm688cYYnqbQkFsH38rCnQuZvWI2j49+HLmNU28XBYpIURqvWH0sr7zyCqmpqUyePLnV/fGHwpiJEu/q/CvLauMDTa0/kjL47O7s2+PFENVI3e5G0UDRdRQNZF3HoIGig7GVAzDRQ4/a6bIBBUKKRFiRiCgSUUPNQzXIqAYJzSCjG2semGQwKkSrE7laq8a/cCGS2YRktSBZLchOJ4a+A5BifOu4qfTKXaiKhNKCmmGxpqohPJ6NAAzo/3iLgpVasmxgwIA5rPhuAtu3zyIn5/+Ouq3fvxd/YDcOe18sloxDq5wS2Vf4GomJo446uiMI7alZAcvBgwe57rrr6i0xXLRoUd0Sw23btjFjxoy6JYYPPPAA06ZNq9dG7S2jWldeeSWlpaU8/PDDHDhwgCFDhvDJJ580mmxOaL10RzqzRs1i+tfTCUQDPD768TYdaSlVS8mzt/xTrKZpvPLKK1x//fUxSc9fcKASgOT42AYsbZEIUa69JaTVjzy6psfR9feNF5LUdR1N1YmqWs0jqhGJ6qiqRkTTUFWdqKahBqLolSHC8Wa0Q9tpUY2AJ4SkyKgRFTWsoYZVtIiGHlHRIxpENKRDDyWqYfRHMUQ0jKqOMapjjur0Vk1YVRMVOw/vWQSoIGncEqzjxrfqe9RSUvV+QjY7tk6QZr+g4EWCof2cNvRdTKbWz/WxWDLo13cmm7fcR0LCcNLTLz1im927n2XX7qcOfSXRLesmevW6j7y8v7Nu3S2sWn0ZuYNfwOHo3BPThRNfs/7qx2KJYW2W3MPdeeed4hZQBzm/5/lYDBZ+v+z33PHZHTx37nPYWpmJtCGhSAiP5Kkr7tgSS5YsoaCggJtuuikmfdpfWpM5Nz0lISbtQdslQlSUQ5Nu1ZYVS5QkCcUgoRhkOnL2l65q6EE/us+H7vMS2b2H8k9NSB1YfkDxlBJtRjHGtqKqIYqK36NLlwtxuU6JWbvp6ZdScvAjdu3+yxEBy759r7Fr91N073YbXbteQ8nBD9m1609UVa0kO/t28vL+xqpVl7Bh412MOKN9CmQKwtGI8psC53Q7h+fHPc/m8s3c8ukthNRQ4zs105biLeiS3qolzePHj0fX9VYvZa51oLwKgKzU1q/sqPXnP/+ZW265hRtvvJGcnByef/55bDYbL7/8cqvalY4ywnK8kRQZ2e5ASe2CoUcvJFvN/CElJXbXoLmMXjfaUfKtzJkzh2HDhuF0OklNTeWiiy5i27ZtR2y3fPlyzjnnHOx2O3FxcYwZM4ZAoOn5ZjQtyqbN0wiHD5KVeV2Lz6UhpaWLqar6HovlyNpAxQfeIzVlEr1734fV2pXs7rdx6ilvArB+wy9ZteoSAFJTm5boTxDakghYBACGpA7h5Qkvs7ViK4+veDzmpes3F28GYGDGwJi22xoVVdVoOmQkx8ekvdpEiIcnSWxKIsSmMNSOsBznActPqWVVABjSm1aMM9Z0TcUcCKHHN7y0/8svv+SOO+5gxYoVLF68mEgkwvjx4/H5fHXbLF++nIkTJzJ+/Hi+//57Vq5cyZ133tnkicmaFmbLlvspK1vCoEHP4HTGbrXS5s2/Y/2GX5KQMJIheUdml45GPZjN9YO1+PjTOO20/zDijM8YPOivDB36Nj17TDtiX0Fob8dHikyhXQxKHsQjIx/hgWUPkG5P5/Yht8es7fyKfBRNoWdq86rotqVqt4ewZIpZnqCWJkJsito+/nTSbSwcXqfpcLfffjuzZs3ikUce4dNPP6WgoICUlBQuuugiZs2ahcvlavWx1Uo/MhEkV8dMeA1XbcOs6SiJDY/a1ZaCqPXqq6+SmprK6tWrGTNmDADTpk3j17/+db1SIk2tZaTrGl8srclLlDPgSVKSz23JaTSorOwLig/UrKbLHfzcEUUMS0o+JBAoICHhyDlQkiRhs2Vjs2XHrD+C0FpihEWo58JeF3J73u3MXzef/d79MWu3wFOAS3fFZLJsrPh9PjTD8ZHPRzbUjLC0xS2hlStXUlxcXPdYvHgxAJdffjlFRUUUFRXx5JNPsnHjRl599VU++eQTbr755pgcW60OoxiqflwG1c4ipT8AoKQMatL21dXVQE0NNahZiPDdd9+RmprKyJEj6dKlC2eddRbLli1rUnt+/24AunX7BenpFze3+8cUCtXkt+qRfVe9EVOfbydbtsxg46ZpdOkyheQYBkmC0JY6z7uH0Glc1Psinlv3HLuqdtHVceR975Y4EDxAiqF1S5pjLRL0YTDFboJxSxMhNoWiyERpm1tCKSn1r8vcuXPp1asXZ511FpIk8d5779W91qtXL2bPns21115LNBptdQCqVkdQrOFWtdEa0bKaW5Xm1FMb3VbTNH7zm98watQoBg2qCXB27doFwMyZM3nyyScZMmQI//jHPzj33HPZuHEjffoce86WptXMF4t3DW3NaTQoI+NyQuFS9ux5hpKDH5KSMhGvdwvl5UsxmVLo03s6mZnXx7T+kCC0JTHCIhwhqkcBMCmmmLVZrpaTbu2YeQpHo0eCmK2xC1gOT4RYqzYR4ogRjS89PhZZrl0l1LZzWMLhMG+88QY33XTTUd/IaquqxmK0LOo3YXDF7uesufSKfCIGGYMjs9Ft77jjDjZu3Mhbb71V95ym1azauu2227jxxhs55ZRTeOqpp+jXr1+TJlo7HANwOHIoOfhxy0/iKCRJoWePuzht6LvEOfM4UPwfwqFScgb8H6NGfkm3bjcjy+Izq3D8ED+twhEiagQAYyuSVh0uGA7ikTx0c7VPzaKmUtQgthhnuW2rRIiKoeazRVtPum2sTlNZWRmzZs3i1ltvjc0BJYlgiYnKp19DMsrIZgXJbESyGJEth5LL2SzIVhuS3U7EZEBJcGF0uJDl+nMy1GA51YtuQ5INSNYEJEsSsjUZxZaKbE/DYO+K4uiKZLJDbTBWXUjYaqWxn/Q777yTDz/8kK+++orMzB+Dm/RDk4VzcupPlB0wYAAFBQVNOH0Jg2JHP/QhoS3ExeUycOCf2qx9QWgvImARjlD7yVrTW5bz46e2FW8DCXon9Y5Je7EQCEcw6xHi42KbNK6tEiEq7bRK6Fh1mtxuN5MnTyYnJ4eZM2fG5Hj2/iqBfJVwuRldNaJpRnTdjI6FHweAVcADeNB0jY8KX8QXrcYoq5gVHZMBTEYZk+YlxeRmTNpuJB2OdqNDlSWiJgNRkxlHwI8v+egjf7quc9ddd7FgwQKWLl1Kjx496r2enZ1NRkbGEUudt2/fzqRJkxo9f1UN4fasp1fKbxvdVhBOdiJgEY7gNNXUHKkOVcekvS0lWwAYkN66Ks2xVFBSiSxBUkLrV7r8VFskQlTaIQ/Lseo0eTweJk6ciNPpZMGCBRiNsRl9c177MxqqcKNHI+h+D7rXg+71ovl9FK0oxLAnhVFj80B2E/b7CAX8hANBQsEQRaU6+6vsjHl2FUgQCZQQ9e5H9Rej+g+i+Q+i+UvBVwaBCmR/FZGgB2nINUft3x133MGbb77JBx98gNPp5MCBAwC4XC6sViuSJPG73/2ORx55hLy8PIYMGcJrr73G1q1beffdd4957qoaYvPm36LrUZKTz2nNt1EQTgoiYBGOkGRJItWWypeFX3Ju99avINhZvhNFU+jdpfOMsNRluU2OXZbbtmRQ2v6W0NHqNLndbiZMmIDZbGbhwoVYLC0vXtlUksGIFJcIcT8mlAuuqcKmawy88bfIhiMz4/5n3i0Etu5HVmr+rBntXTHaWzdpfP78+QCMHTu23vOvvPJK3W2z3/zmNwSDQaZNm0ZFRQV5eXksXryYXr16NdhmKFRKaemnFO5/nUCggEGDnsFm69HgtoIg/EgELMIRJEnipkE38Yfv/8Cg5EFc1veyVhVGLHAfWtKsdJ4ft5KymtGjrC7HR8BSV/ywjeKVo9VpcrvdjB8/Hr/fzxtvvIHb7cbtdgM1q4sUpf1S6keqAoR0rcFgBcBbXoU1PrbL1JuaQHH69On18rA0pLz8S7Zue5hgsBBJUkhIGMnAgfNwOvrHoquCcMLrPO8gQqfys34/Y1fVLmatmMW/tv6L3JRcspxZdI/rTp/4PmQ5s1Dkpr1ZFQeLSTa0vpBbLJVVVaPrkJ4U39FdaRKldl4RbROxHK1O05o1a/juu+8A6N27/gjZ7t27yc7ObpP+NET3qESUoy+B9lcFSOzWcSn+jyYYLGbb9pmUlS0hIWEEvXvdR0LCCEymztdXQejMRMAiNEiRFR4a8RATe0zkne3vsLViK4v3LMYT8QBgUSz0ju9Nv8R+nJ52OqMzRxNnangCa5lWxjDnsAZfC4VClJSUsGXLFiwWC0OHDsUR45U7DXG73YQlY6dKZHcsity2k25r6zT91NixY2NepqGl5KCEZjn6RPBgtUZccucKjDUtwtp1NxKJVDNo4NOkpk4WeU8EoYWOj7/WQocZljaMYWk1wYau61QEK9hRtYNtFdvYXrmd9WXreW/Hj4nF7j3tXkZ3HU1PV08kScIX8uGVvGS7slFVldLSUgoLC+seZWVl9Y63dOlScnJyGDlyJF27xiZpXUN8x1GWWzh85VYHd6QDmVULobiGl//6PaWoIZkD+Vv5/K2HsTpcWB3xWB0JWB2J2OJSsMWlYbElxKwUQ1NUV6/B59vB0KFvt0lyOEE4mYiARWgySZJIsiaRZE3ijPQz6p4/4DvAv7b+i5c3vsyTq57kyVVP4jQ6cZld+CI+kCB/dT5zv59LJBJBkiS6dOlCdnY2o0aNIi0tDYfDgdfrZd++fSxevJhNmzahKApnnHEGiYmJJCYmkpSUhNPpjMkn1HDQj9FkbXU77a2zjHa0NzUUwSLZURMbviWkGEw40qC6UGdt/mp07Sg/I5KOYtIYNH4I466d04Y9rmEw1Iw6VlWtIs6Zixyj3EaCcDISAYvQamn2NKYNnca0odPwR/ysK13HpvJNeMIe3EE3pUWljO03lrSUNDIzM8nIyMBkOjK7qdPpJD09nVNOOYUPPviA6upqNm3aRHV1dd0btd1uJycnhxEjRtTVc2kJPRzAHC/mELSWrupES/1Ein0gS1j6JyKbYz8Rt2DRKoyShLVLw8vQzVYXtz39IVAzgTgScuNzH8TvPojfU0bAW0nAW0XQ52bdh8upKjkQ8z42xOHoT0bGleTn/x/Fxe8yeNBfcTgaLrQoCMKxiYBFiCmb0caIjBGMyGh5Knqj0chll11W93U0GqWyspKKigr27NnDhg0bWLduHdddd129rKPNoahBbPa2nysTc51g/oPqDeP/oZTg9grCez3oYbXuNcmkkPLLXEwZrf/ebpz9PpIXdIOOElYwSglknj2k0f1kWcZsjcdsjSexy5HBwdqF52NxOIiEA20+yiZJEgP6P0Fm15+zafM0Nm2+h9OH/VfMYxGEFhC1hIROz2AwkJKSQr9+/ZgwYQJ33XUXycnJfPTRRy1qLxCOYtYjuGKc5bY9dNTbnK7rhArcVLy1leI531P9yW6QJJznZJFyay4ZM0eQescQDIlmKt7a2urjqaEILnfNCJgOaIpGVXIlirl1t1TUaBg1KrHt89385eeXs/7r11rd16ZwOgeQ3f12vN4tVFR81S7HFIQTjRhhEY47ZrOZM844g//85z94PB6czoZypR5dYWkViqSTlCAClsPpqoZnaSHBHZWo7jCySUZJsKC4zIT3eYjs96IkWnBNzMY+tAuyrX7wYMpy4jynGxVvbkX1hlEcLS9q6NlXgiRJOM7KoPvE01t7anUUg4nLZ95PweZlfPfWt4T8npi13RhNq5l/s3bdTZx7Tn67HVcQThRihEU4LvXq1QtFUXj//fcpLS1t1r5FZVUApCbGx75jbUhDb9M7QlXv5+P+vAAlzoQ1JwlTtgs0nfAeN4rDSNINA0m79zScZ2YeEazU9dETBkVCtrTus5B3X83qMVtm7Jcpd+s3hsz+NZPGXSlZMW//aLp0mdJuxxKEE5EYYRGOS3a7nSuvvJIPP/yQv/71r8THx5OWlkZ2djaDBg06Zi6XsooqANJakTTOHYywq9RHYaUfoyKTnWSnT6oDWW6biKJ20nFbxSv+DWX4Vh4g/pLeOE4/ejHAxoQLvRjT7UiG1n0WChRXY0UmLrt1RSOPpqp0DwAJXXq2SfsNURQz/fs9ztZtDxIOl2Eyda6cMYLQ2YmARThu9e3bl1//+tds3bqVoqIiioqKWLx4MV9++SW/+MUvSEpKanC/yuqa1PJdU5uelr+oKsCqvZWs3F3Bd7vL2V7iPWKb7kk2rhuRzc/P6I6plW/YP1WbfyXWkzW1UBTf9yVUf7Iba24y9mFprWovtNeNdUDrV19Fyn1IqozZYW/WfnPnzmXGjBncfffdzJs3D4DbbruNJUuWUFRUhMPhYOTIkVx2bjagE5/SvvWtoqoXSTIhSWJ5syA0lwhYhOOawWBg0KBBDBo0CACv18sLL7zAt99+y5QpDQ/Bu90ewrqC3dJw4jhd11m+q5z/riui0hdhfWEVRdVBAHok2xneI5HbxvSiX5qTrAQbUU1jS7GH99YU8sRHW3h75T6eu/ZUeqXEbhVSLPOv6BGNcKGHwIYyfKtL0CMq9mFpxF/Yq1UBka7rqO7QUW8XNaRs6x6ivgCy2YhsNqCYjSgWE2pJkNBRagYdzcqVK3nhhRfIzc2t9/zQoUO55ppr6NatGxUVFcycOZPfznyZ3507hu8//gtmqwOT1YnZ7sRsdWGxxdc87ImYLHExSzSn6zqFhf+kS+r5GI2xrxIuCCc6EbAIJxSHw0FOTg5btmxB1/UG34B9fh9hueFgxReK8qt/ruGr7aX0SLaTZDcxaXA6w7ITGdo9gRRnw/uN7mNmdJ9kbjmzJ3f9aw3nP/01o3onc2q3eAZnxpNoM5GZYCXeZmxRUFA7wtKat85oWYDK93cS2lMNUR3ZbsQxMgP78HQMMSgaKEkStsEpuL8owNTdiaX3sUewPPtLCbxS0OD3I4EU3P39TT621+vlmmuu4W9/+xuPP/54vdduvfXWuv9nZ2fz+OOPk5eXR4Xfz4o3v26kZR3ZqCMpYHXJXP3oCzgTWpqBWScUKsbh6NfC/QXh5CYCFuGE07dvX7777juKiooaTO8fDvjRj5KW/9kvdrJydwV/u+40xg1IbXZwkZMRxwd3juYfy/ewPL+c+Uvz8R2Wp8SkyCQ7TGQm2shKsNGni4NRvZIZnHnsT9w6ek3ZwxYOgISLvJS9tBHZasA1qQfmbFfNXJMYz7lJuLQPqjtE5bs7SPvtUCTj0UdJfPvLkCSJYK6GLSMRLRJFC6voYRVdgr7nD2/yce+44w4mT57MuHHjjghY6h3T5+OVV16hR48ePLpgERIRgr4qgv6Kmn8D1YR8bsIBD6GAj3DQSyTgp2B9PpUFAXzu0hYHLJIkk5R0FgdLP6V791sb30EQhHpEwCKccLKzs4mPj+fdd98lJyeH1NRUHA4HDoeDhIQEIuEQiqHhJbefbznIlLx0zstp+WRPh9nA7WN7c/vY3oSjGqXeEOXeEIWVAUo9IUrcQfZVBthZ6uXjjcXM/XgruZkubhiZzYV5GRiUI8dRau8ISS2MWKo/3o1sM5DyyzwUe9vNn5AMMvEX9qLkqTUEd1RhzWl4HhFAqMqHAiSd1pOEvi1frfPWW2+xZs0aVq5cedRtnnvuOe677z58Ph/9+vVj8eLFh7ItmzCa7I0GIcs/fJJvX19KfHL3FvcTIClxDNt3PIbfvwebLbtVbQnCyUYELMIJR1EUrr76apYuXcr69evxeH7MtWE0GrFEIlRauxJRNYyHBQcHPUG2lXi4dUzsVo6YDDJd4610jbeSmxl/xOtRVePL7aX8Y/le7nl7HU9/toN7zuvL1CH130DrApYWxCvR8gCh/Crip/Zu02ClliHVBkBgY9kxAxYOjTyFqn0tPta+ffu4++67Wbx4MRaL5ajbXXPNNZx33nkUFxfz5JNPcsUVV/DNN98cc5/D+arKkQ0aFnvTJ2o3JC3tYvYWvMju3c8wcOCfWtWWIJxsRMAinJBSU1O54oorAAiHw3i9XrxeL1u2bGH5yjVsdRs47fEljBvQhfEDu2A2yLy0bDcuq5FzB6S2Wz8Nisy5A7pw7oAubNxfzbwlO7j7rbV4Q1GuGf7jp3ntsGXNoT3VRMuDmLo6MHSxHfO2leoNU/76ZhSXGdsp7XNedf1pJLrKGJPLnq+WEvqojLRh/Vt0rNWrV3Pw4EFOPfXUuudUVeWrr77i2WefJRQKoSgKLpcLl8tFnz59OOOMM0hISGDBggVcddVVTTqOv7oSo61FXazHYLCT1mUq+4v+ha6rSFLs6y4JwolKBCzCCc9kMtVVfO7WrRvjx49nU5GbRZsO8PHGA7y3phCALnFm/nhZLvG2lmdobY1BXV38/frT+PW/fuClZbvrBSw6oCDRbVUZpavK6p5XEsxYc5Iw94xH1zTU6jB6MAqShBaI4ltVgiRDym25yKb2e3M0ptmRDMcOWIxxVqJ9JOJ2JlK6Pp+U3F7NPs65557Lhg0b6j1344030r9/f+6//34U5chz1nW9ptRAKNTk4/jdXkz22Hz/kpLOYs/e53B7NuKKy4tJm4JwMhABi3DSkSSJQV1dDOrq4rfj+1FQXrMaJSvR2imK0o3qncTCdUUUVQXIiLei6zrvrtpHPFH6dYun5/k9MWY4CO9xE9hcjn99Gd5vigCQjDKSRamJcCQJ+6mpOM/JalWa/JYwdLERLmr8Vk/vq8ey69HP8PxrA0kDeyA3MH/nWJxOZ92S9lp2u52kpCQGDRrErl27+Pe//8348eNJSUmhsLCQuXPnYrVaOf/885t8nKDHj9nR+pVU8GOKflnqmMBYEI5XImARTnrdkmIw1h9D43PSmLdkB1P/+g2XDc1kb7mPjzYc4Orh3Zh84cC6eTeWvglY+iYQf2EvNF8ESZGQrIZOEXRZByRS8dY2Igd8GNOOnvzNaLdgGpOEfVmEnW8upe/Pz4lpPywWC19//TXz5s2jsrKSLl26MGbMGL799ltSU5t+iyzkDZPQ9dgJ8VTVT2npEvz+3ewteJ6MjJ/RNeNn2Gy9kGUDuq7j8W4if9eTmE1dxPJmQWgmSY9lRqoO4na7cblcVFdXE3ccVuAVhJ86UB3kyU+3sXTbQRRZ4vfnDzhiIm5npkc1Sp5ajWQ1kHzDwCNGeHz7yin6YgPhndU4QnEokoHKpAoG/25qB/X42J65+XwSujk5ZcJFGExWjEYLRrMNo8mGwWRDkyvZnn8/oVAxJlMK4fCP9a0kSUFRnKiqB11XsVq6MXDgU7hcQzruhAShk2jO+7cIWARBaBPhQg9lr2wEJFyTexAiQNm3O2B/FJvuQNVV3FI5UncLXcYMIGVg+9X1aa6/3nY+wapj364a9guJ00b9BZutBwCRSDVe7xZ8vp1Eo14MBicWa1cSE0Yjy2JwWxBABCwd3R1BEA5RPWGqFuYT2FAzUTgQ9VAhl+LMSyfznDzi0tumuGGshQLVeCoLiYQDRMJ+oqEA0UiASCjAJ/P+CcC0NxcgK6JGkCA0R3Pev0WYLwhCm1GcJpKuGUD5hj18/vILFBTVrOixVcWjfaGhRSNIsozRYkWWFVypXRgy/nx6DxuB3MAKn45itrowWxvORrzy3a/p2j9HBCuC0MZEwCIIQptLGpzN5U/NIej1snvtKqpKipFlBcVoRFNVIqEgWjTK/m1b+O9Tc7G54gG4/sm/YovrvIUCdV0n6PVgdXbePgrCiaJZawjnz59Pbm4ucXFxxMXFMWLECD7++OO61w8cOMDPf/5z0tLSsNvtnHrqqbz33nvHbHPmzJlIklTv0b9/y5JICYLQuVkcDgaMHsuIS69i+MVXcNoFF3P61MsYdcW1nHn1Dfzs0T9w7Zx5KAYj/uoqXr/vLqLhcEd3+6g85WX4qipJ69Wno7siCCe8ZgUsmZmZzJ07l9WrV7Nq1SrOOeccpk6dyqZNmwC47rrr2LZtGwsXLmTDhg1ccsklXHHFFfzwww/HbHfgwIEUFxfXPZYtW9byMxIE4bjWpWdvbn3uFS6d8SgBr4el//gbnXWq3aYvl6AYDGT0G9DRXRGEE16zApYpU6Zw/vnn06dPH/r27cvs2bNxOBysWLECgG+//Za77rqL008/nZ49e/Lggw8SHx/P6tWrj9muwWAgLS2t7pGcnNzyMxIE4YSQPWQo59xwG+sWf8zyd//V0d1p0PYV39B/9NhOfdtKEE4UzUsreRhVVXnrrbfw+XyMGDECgJEjR/Lvf/+biooKNE3jrbfeIhgMMnbs2GO2tWPHDjIyMujZsyfXXHMNBQUFx9w+FArhdrvrPQRBOPHkjpvI6KuuZ/m7b7Lsrdc73UhL0OvBmZTS0d0QhJNCsyfdbtiwgREjRhAMBnE4HCxYsICcnBwA3n77ba688kqSkpIwGAzYbDYWLFhA7969j9re8OHDefXVV+nXrx/FxcU8+uijnHnmmWzcuBGn09ngPnPmzOHRRx9tbtcFQTgODb/ocmRZ5qt/vkJ54V7GXvcLXKlpHd0t1GiUcMCPwShWBwlCe2h2HpZwOExBQQHV1dW8++67/P3vf+fLL78kJyeHu+66i++//54nnniC5ORk3n//fZ566im+/vprBg8e3KT2q6qq6N69O3/+85+5+eabG9wmFArVK1zmdrvJysoSeVgE4QS2/btv+Oyl+fjd1XQfPITccRPpfdoZHbb8+YdFH/LFKy/y8//7CyndsjukD4JwvGvXxHHjxo2jV69e3HffffTu3ZuNGzcycODAeq/37t2b559/vsltDhs2jHHjxjFnzpwmbS8SxwnCySESDLJtxTLWf/YJxdu3kpiRyaUPPEZcctPrAsXKvx+djslq4+L7Hm73YwvCiaI5798tnsNSS9M0QqEQfn9NxVtZrt+koihomtbk9rxeL/n5+aSnp7e2a4IgnGCMFguDxo7j6llPcu2ceURCIZb8/bl274e7rJT9WzfTY8hp7X5sQThZNStgmTFjBl999RV79uxhw4YNzJgxg6VLl3LNNdfQv39/evfuzW233cb3339Pfn4+f/rTn1i8eDEXXXRRXRvnnnsuzz77bN3X9957L19++SV79uzh22+/5eKLL0ZRFK666qqYnaQgCCeeLj17M/ziK9izbg0hv6/djhsOBvj0hb9gc8UzYPRZ7XZcQTjZNWvS7cGDB7nuuusoLi7G5XKRm5vLokWLOO+88wD46KOPmD59OlOmTMHr9dK7d29ee+01zj///Lo28vPzKSsrq/u6sLCQq666ivLyclJSUhg9ejQrVqwgJUXMvBcE4diyBg5G1zQO7NxB99whrWpL13XK9+2lYOM6fNVVZPQdgBaN4quqxFddia+qksri/ZTsykdTo1x038OYbfbYnIggCI0SxQ8FQThu6ZrGc7dcw6Czz+Osa29q1r7RcJiyfXs5uCeffZs2sG/TenxVlSgGA2a7A391FQCyomCLT8DuSsCVkkpqz970HzkGV+rxUbhREDozUfxQEISTgiTLDBw7jrWL/scpEy845uTbSDhE0dYtFGxcS8Gm9ZTs2omuaUiSTGqPXuScdS7dBw0ho/8ADEYT7tKDmKxWLHYHktzq6X6CILSSGGERBOG45ikv4/XpdyNJErnnTiC5WzYmixVZMeCrqqCyeD9F27ewf9sW1EgEmyuerIG5ZOUMIrVHL5KzumM0Wzr6NAThpNSuy5o7AxGwCMLJzVdVyTf/fp1ty5cRDvjrvWZzxZPWuy/dBubRbXAeyVndkSSpg3oqCMLhRMAiCMJJSdd1Qn4fkWAQNRrF7orHaBGjJ4LQWYk5LIIgnJQkScJid2CxOzq6K4IgxJiYSSYIgiAIQqcnAhZBEARBEDo9EbAIgiAIgtDpiYBFEARBEIROTwQsgiAIgiB0eiJgEQRBEASh0xMBiyAIgiAInZ4IWARBEARB6PREwCIIgiAIQqcnAhZBEARBEDo9EbAIgiAIgtDpiYBFEARBEIROTwQsgiAIgiB0eidEtWZd14GaMtWCIAiCIBwfat+3a9/Hj+WECFg8Hg8AWVlZHdwTQRAEQRCay+Px4HK5jrmNpDclrOnkNE2jqKgIp9OJJEm43W6ysrLYt28fcXFxHd09oQHiGnV+4hp1fuIadW7i+jRO13U8Hg8ZGRnI8rFnqZwQIyyyLJOZmXnE83FxceKHpJMT16jzE9eo8xPXqHMT1+fYGhtZqSUm3QqCIAiC0OmJgEUQBEEQhE7vhAxYzGYzjzzyCGazuaO7IhyFuEadn7hGnZ+4Rp2buD6xdUJMuhUEQRAE4cR2Qo6wCIIgCIJwYhEBiyAIgiAInZ4IWARBEARB6PREwCIIgiAIQqd3wgUs27dvZ+rUqSQnJxMXF8fo0aP54osv6m0jSdIRj7feequDenzyaco1qlVeXk5mZiaSJFFVVdW+HT2JNXaNysvLmThxIhkZGZjNZrKysrjzzjtFPa920tj1WbduHVdddRVZWVlYrVYGDBjA008/3YE9Pvk05e/cr3/9a4YOHYrZbGbIkCEd09HjyAkXsFxwwQVEo1E+//xzVq9eTV5eHhdccAEHDhyot90rr7xCcXFx3eOiiy7qmA6fhJp6jQBuvvlmcnNzO6CXJ7fGrpEsy0ydOpWFCxeyfft2Xn31VZYsWcIvf/nLDu75yaGx67N69WpSU1N544032LRpEw888AAzZszg2Wef7eCenzya+nfupptu4sorr+ygXh5n9BNIaWmpDuhfffVV3XNut1sH9MWLF9c9B+gLFizogB4KTb1Guq7rzz33nH7WWWfpn332mQ7olZWV7dzbk1NzrtHhnn76aT0zM7M9unhSa+n1uf322/Wzzz67Pbp40mvuNXrkkUf0vLy8duzh8emEGmFJSkqiX79+/OMf/8Dn8xGNRnnhhRdITU1l6NCh9ba94447SE5O5vTTT+fll19uUmlrofWaeo02b97MY489xj/+8Y9GC2IJsdWc36NaRUVF/Oc//+Gss85q596efFpyfQCqq6tJTExsx56evFp6jYRGdHTEFGv79u3Thw4dqkuSpCuKoqenp+tr1qypt81jjz2mL1u2TF+zZo0+d+5c3Ww2608//XQH9fjk09g1CgaDem5urv7666/ruq7rX3zxhRhhaWdN+T3SdV3/2c9+plutVh3Qp0yZogcCgQ7o7cmnqden1jfffKMbDAZ90aJF7djLk1tzrpEYYWma4+Kj6/Tp0xucKHv4Y+vWrei6zh133EFqaipff/0133//PRdddBFTpkyhuLi4rr2HHnqIUaNGccopp3D//fdz33338cc//rEDz/D4F8trNGPGDAYMGMC1117bwWd1Yon17xHAU089xZo1a/jggw/Iz8/nnnvu6aCzO/61xfUB2LhxI1OnTuWRRx5h/PjxHXBmJ462ukZC0xwXqflLS0spLy8/5jY9e/bk66+/Zvz48VRWVtYr5d2nTx9uvvlmpk+f3uC+//vf/7jgggsIBoOi5kMLxfIaDRkyhA0bNiBJEgC6rqNpGoqi8MADD/Doo4+26bmcqNr692jZsmWceeaZFBUVkZ6eHtO+nwza4vps3ryZs88+m1/84hfMnj27zfp+smir36GZM2fy/vvvs3bt2rbo9gnD0NEdaIqUlBRSUlIa3c7v9wMcMedBlmU0TTvqfmvXriUhIUEEK60Qy2v03nvvEQgE6l5buXIlN910E19//TW9evWKYa9PLm39e1T7WigUakUvT16xvj6bNm3inHPO4frrrxfBSoy09e+QcGzHRcDSVCNGjCAhIYHrr7+ehx9+GKvVyt/+9jd2797N5MmTAfjvf/9LSUkJZ5xxBhaLhcWLF/PEE09w7733dnDvTw5NuUY/DUrKysoAGDBgAPHx8e3d5ZNOU67RRx99RElJCcOGDcPhcLBp0yZ+97vfMWrUKLKzszv2BE5wTbk+Gzdu5JxzzmHChAncc889dUtpFUVp0huu0DpNuUYAO3fuxOv1cuDAAQKBQN0IS05ODiaTqYN634l13PSZtrFy5Up9/PjxemJiou50OvUzzjhD/+ijj+pe//jjj/UhQ4boDodDt9vtel5env7888/rqqp2YK9PLo1do58Sk27bX2PX6PPPP9dHjBihu1wu3WKx6H369NHvv/9+cY3aSWPX55FHHtGBIx7du3fvuE6fZJryd+6ss85q8Drt3r27YzrdyR0Xc1gEQRAEQTi5HRerhARBEARBOLmJgEUQBEEQhE5PBCyCIAiCIHR6ImARBEEQBKHTEwGLIAiCIAidnghYBEEQBEHo9ETAIgiCIAhCpycCFkEQBEEQOj0RsAiCIAiC0OmJgEUQBEEQhE5PBCyCIAiCIHR6ImARBEEQBKHT+38oM3RSE3/2pwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Build county geom before we aggregate stats by county\")\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "nTracts = nDrawnDistricts\n",
    "tractGeom = tractPopFile['geometry']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "print(\"We will now build the county-by-county geometries\")\n",
    "skipList = [8197, 4634, 3730, 4458, 840, 2974, 1008]  #a previously known coastal list\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "print(\"Here is your original county-based map b4 any triage\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "29fac5ad-5d24-451b-897a-c1a58595942b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now plot counties with GOP seat expectations that swing with VRA applicn\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countySwing = [0.]*nCounties \n",
    "print(\"We will now plot counties with GOP seat expectations that swing with VRA applicn\")\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    for t in countyTractList[c]:\n",
    "        countySwing[c] += tractPop[t]/countyPop[c] * ( SeatExp[t] - SeatExp2[t] )\n",
    "    if abs(countySwing[c]) > 0.001 :\n",
    "        plotCenter(r3(countySwing[c]),countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "270aa532-b269-446a-a7c5-1298b29fd7ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OH has total pop 11799448  =  786629.867 per district\n"
     ]
    }
   ],
   "source": [
    "aDP = statePop/float(nStateDistricts)\n",
    "print(STATE,\"has total pop\",statePop,\" = \",r3(aDP),\"per district\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3690d3bd-ac7f-4248-a2e0-918b4f019482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region name, total number of districts swung to GOP by applying VRA\n",
      "NW 0.01082\n",
      "NE 0.26818\n",
      "SE 0.00013\n",
      "SW -0.1587\n",
      "mid -0.02086\n"
     ]
    }
   ],
   "source": [
    "regionName = [\"NW\",\"NE\",\"SE\",\"SW\",\"mid\"]\n",
    "nRegions = len(regionName)\n",
    "regionPop, regionSwing = [0.]*nRegions, [0.]*nRegions\n",
    "cLists = [ [85,25,47,19,34,86,61,71,62,68,31,73,87,16,80,1,32,53,5],\n",
    "          [21,38,69,46,2,17,51,84,37,15,76,75,78,42,27,66,3,77,49,14,9,33,40],\n",
    "          [59,29,6,63,57,60,55,36,81,4,83,39,52,26,43],\n",
    "          [18,54,11,67,56,28,23,8,82,13,35,70,65,30,12,7,0,72],\n",
    "          [ ] ]\n",
    "totClist = cLists[0]+cLists[1]+cLists[2]+cLists[3]\n",
    "for c in range(nCounties):\n",
    "    if c not in totClist:\n",
    "        cLists[4].append(c)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    for i,L in enumerate(cLists):\n",
    "        if c in L:\n",
    "            plotCenter(regionName[i],countyGeom[c])\n",
    "            regionSwing[i] += countySwing[c] * countyPop[c] / aDP\n",
    "plt.show()\n",
    "print(\"region name, total number of districts swung to GOP by applying VRA\")\n",
    "for i in range(nRegions):\n",
    "    print(regionName[i],r5(regionSwing[i]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f394b039-4766-4bf4-a001-d4c0a8fc2b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total GOP gain in total districts by applying VRA is 0.09957\n",
      "0.09957268871376757\n",
      "0.6546183033213169\n",
      "0.6479801240737351\n",
      "map seat fraction swing = 0.00664\n"
     ]
    }
   ],
   "source": [
    "print(\"the total GOP gain in total districts by applying VRA is\",r5(np.sum(regionSwing)) )\n",
    "print(np.sum([countySwing[c] * countyPop[c] / aDP for c in range(nCounties)]) )\n",
    "print(np.sum([SeatExp[t]  * HDwt[t] for t in range(nDrawnDistricts)]) )\n",
    "print(np.sum([SeatExp2[t] * HDwt[t] for t in range(nDrawnDistricts)]) )\n",
    "print(\"map seat fraction swing =\",r5(np.sum(regionSwing) / float(nStateDistricts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7942ff2a-f694-4032-b877-a877bbfa43a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3ba6259-bcda-448c-8830-660eca0c1503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below, we plot votes-to-seats curves for geog bias visualization\n",
      "AZ has HD-average lean 0.49978 which may differ from state vote due to turnout effects\n",
      "PA has HD-average lean 0.50227 which may differ from state vote due to turnout effects\n",
      "TX has HD-average lean 0.52825 which may differ from state vote due to turnout effects\n",
      "WI has HD-average lean 0.49467 which may differ from state vote due to turnout effects\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Below, we plot votes-to-seats curves for geog bias visualization\")\n",
    "shortStateList = [\"AZ\", \"PA\", \"TX\", \"WI\"] #[\"AZ\", \"FL\", \"MI\",\"PA\", \"TX\", \"WI\"]  #too busy a plot\n",
    "STYLES = [\"dotted\", \"solid\",\"--\",\"solid\"]\n",
    "WIDTHS = [1,0.5,1,1]\n",
    "stateHDwts, stateHDleans, stateAvgHDlean = list(),list(), list()\n",
    "for i,STATE in enumerate(shortStateList):\n",
    "    infile = \"./2024state_HD_output/\"+STATE+\"_HDstats2.csv\"\n",
    "    inDF = pd.read_csv(infile)\n",
    "    stateHDwts.append(inDF[\"HDweight\"])\n",
    "    stateHDleans.append(inDF[\"GOPlean\"])\n",
    "    plt.hist(stateHDleans[i],weights=stateHDwts[i],bins=50,label=STATE,histtype=\"step\",ls=STYLES[i],lw=WIDTHS[i])\n",
    "    stateAvgHDlean.append(np.sum([stateHDwts[i][j] * stateHDleans[i][j] for j in range(len(stateHDwts[i])) ]) )\n",
    "    print(STATE,\"has HD-average lean\",r5(stateAvgHDlean[i]),\n",
    "          \"which may differ from state vote due to turnout effects\")\n",
    "plt.legend()\n",
    "plt.text(0.67,0.004,\"AZ\",color=\"blue\",fontsize=15)\n",
    "plt.text(0.11,0.015,\"PA\",color=[0.8,0.4,0],fontsize=15)\n",
    "plt.text(0.78,0.025,\"TX\",color=\"green\",fontsize=15)\n",
    "plt.text(0.26,0.06,\"WI\",color=\"red\",fontsize=15)\n",
    "plt.xlabel(\"district GOP vote share\")\n",
    "plt.ylabel(\"weighted frequency in collection\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ecbd71c7-6ae1-4934-89f8-a14f6273f6f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nDistricts 9   nHDs\n"
     ]
    }
   ],
   "source": [
    "print(inDF[\"params\"][0],inDF[\"params\"][1],inDF[\"params\"][2],inDF[\"params\"][3] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "09c84550-714a-4a34-904f-4df135a8c26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute and visualize the turnout bias\n",
      "AZ has statewide lean of 0.5086 vs district avg lean 0.49978 for a turnout bias of 0.00883\n",
      "PA has statewide lean of 0.49949 vs district avg lean 0.50227 for a turnout bias of -0.00278\n"
     ]
    },
    {
     "data": {
      "image/png": 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XFBRg9OjRGDx4ML744gtMnz7d9LkXLVqEhQsXBr43NTWxSGIYKxBFi9NYZYfo7dm61Vpvj1vKCVAWgW6xjWEswjEPUq9evZCYmIiGhoag7Q0NDejbt6+uNLp06YKxY8di3759qvvk5eWhV69egX369u0bEgTu9/tx4sQJzfOmpKQgIyMj6MMwDBOC27w9duDGLj+GsRjHBFJycjIKCwtRWloa2NbR0YHS0tIgL5EW7e3t2LVrF/r166e6z1dffYXjx48H9pk8eTJOnjyJ8vLywD7/+Mc/0NHRgYkTJ5rMDcPEObEaj2I2X24KgLbj2sSDCGTiHkdHsS1cuBBLly7Fm2++ib179+IXv/gFfvjhB9x0000AgDlz5mDRokWB/R955BGsXr0atbW12LZtG66//nocOnQIN998MwAhgPuuu+7Cxo0bcfDgQZSWlmL27NnIz8/HjBkzAADDhw/HzJkzMX/+fGzevBn//Oc/cdttt+Hqq69GdnZ29AuBYbxOJPEobhZW0YqzMVMGeo+xMw9uEoEMYwOOCqSrrroKTz/9NB544AGMGTMGFRUVKCkpCQRu19XV4ejRo4H9v/vuO8yfPx/Dhw/HpZdeiqamJmzYsAEjRowAACQmJmLnzp2YNWsWhg4dinnz5qGwsBDr169HSkpKIJ0///nPOPfcczF9+nRceumluOCCC/DHP/4xuplnmFjBbFCy1Y23F4bWyzFTBkaO4RmzGcY0PiIip43wIk1NTcjMzERjYyPHIzHxjdhgi0HJertcqquFRl6kqkrwSPj9QkOel6ffOyG1oahI6P6J1LNhNl9GUCsDq44xmAcxVjM/P19vDhjGc+htvx1faoRhGI9jNh5FKdDXrFfJLUPrjXqxzAQ7GzmGY4UYxjQskBiGiZykJKGhrq3VLw6UGm+zQseuUVVG4mzMiDs1AaMktMRtgDHRw7FCDGMKFkgMw0SOWc+PvPE2K3Si6SlR8xKZFXfyMlAqS/k2gEUPw9gMCyQmfnHzCCqvoSYOjJZxJEInGp4SLSGYlwcUFgr/FxWZ92IplaVXgq3F693ays8W43lYIDHxiVNLJcSqKNMbT6Qn/1YKHadGtkUy9iU3Fxg1SvhfLEsvTMwovd49e/IyJIznYYHExCeRDE03+4Ycy+tX6Yknqq6Obv6jvWhsbS0gTkBbXm7Oy+P3A1OnArt3CyJp3TqhLL0QbC293qdOCX/d7O1imDCwQGLiEzNv5JG+IXulm8Qs4eKJgOjm30h56/U0aQkVK7w8Upt37wbq6oLP7ea4I2n+09OFv271djGMDlggMfGJmTfySN+QvdBNYiXyMh46VLkbzq4uR73lbdTTpCZUrPDySG0uKBC625Ts1TuLdjS7c6X5P37c3d4uhtEBCyQmfjH6Rh7pG7IXukmsRlrG8vwDncJk7Fih29Lqc+spbzd59pKSgM8/B4YMAXbtErrbRIHj9wN79gCTJoUXc05154rXOzXV3d4uhtEBCySG0YsVb8hu7yaxG2n+5d1J48db35DrKW+rPHtWiBIxjZoa4bso2MTtI0d2xjlpiTk3iT6G8SgskBjGCPyGbB15eZ2jtQBBJDnRkFvh2fP7gdLSyEVJba1QDiIjR3Z2lYlpi2iJuXjrzmUYG2CBxDCMMyQlAVu2hA5pd8oWs4JX9O7MnBl5cHJuriCKRGprhe9z5gTPsVRZqS3m4rE7l2Eshp8ahmGcIzUV2L7d+OK0bkIevF9SAkyfbjwv4hD/ysrObS0twt/ycmG7uKSLnrRF0ccwjCnYg8QwTHjsHBFlZh03NyHvzjIjjoBgoSUi9UgNHcrdugwTRVggMe4nVmef9gp2j4jy+gSaVnVnSYWW2I3Gw+UZxjFYIDHuxuuNZyxg94goq9OXCmoviWup0CorA0aM4MEADOMgLJAYd8PDla0hEqGQm9vZ1ZOerjx5YSRYMeJKugSMOE/QxIn65gyKFCtFfLhgcS8JPobxOCyQGHfDw5UjJ9IGvK6uc+bwU6eCl7+wgki7qKT5KyrqnCdo2zZ9cwZFipKIt0PIsDeVYaIKCyTG3fBwZXX0NsKReuH0iNRIBUEkw+yl+ZOOAAM6h8zbKa7l5ZOba4+QYW8qw0QVFkiM+4n32aeVMOJNiNQLF06kOu3ZkAc3S+cL2rrVfnEtL5+6OnuEDHtTGSaq+IiInDbCizQ1NSEzMxONjY3IyMhw2hxGC7/f2/PsKFFdLQgSkaoq7Tlv7CwDo7bYgTR/gLPXWxSMW7cKQkYqziK9Djbfy/v27QMA5OfnW542w7gFve03e5CY2MZp74ZdGPUm2OmFc4NnQ74ort0eR60uRdGjVFkJrFgRfEyk96JS3vR2b3KAd3iUyojLLW5hgcTENrEat+Gm2Cw32RIN9AqduXOFGChxn3D3opmGWK8tavtx49+JUhnF6gsWowsWSExs4wbvhl24KTbLTbbYjR7RrbSP1r0ob4hbW60NwFcbaceNfydKZRSrL1iMLlggMbFNvHk3GOuRe1n0iG61fd58M3ShWb8fKC0NbojHj9fnFfL7O4PStV4AlOzhxj8YpTKK5RcsJiwskJjYxyrvRjx1R8RTXrVQ8rLoEd3yfQDh+JEjha43Me09e4TJLGfO7JyMs6AA2L1b+F8+r5LoWWpt7UwPCBVd4ewR17/jxr8TpTLiF6y4hgUSw+ghnroj4imv4VDzsugR3dJ95OlUV3cKHHEyy1OngJISYPNm9XmVevYU/o4e3ZleeTlw+HD4vMhtljb+69Z5d7FgK1G6rvHUfcwEwQKJYfQQT90RXsurnd6uSLwsUrvk6QCdZSwyfjwwfbqw/pravErijOY1NUBamvB/errggdIrZqV2iZ6kqVMF4fXTn7JIYpgfYYHEMHqIp+4It+RVj/AxG9ysFyNdLPJFcqV2AcHpDB3aGTtUWBjaRSb1WkivhyiKAKClBXjssU7RpEfMSu0aO1YoL6kg3r1bnzfKbriLl3EBLJAYc8RbBRZPsQhuyKvebj65t0tPcLNR9HSxyO2trg71wsnTEefoJeoMmtaaV6mqCjhyBBgyRNieng7cd19n7JIeMSsXQ6NHA9nZnQKsoADIyQlfJnZiZRevXWvixVPdF8ewQGKME68xKvEUi+B0XvV280m9K0rBzVai1TDK7QWUvXBiGnv2CIvpAsLfoqLQ+Xek5xI9Sf/+70L32pAhwQsIl5ToE7N5ecCoUZ3fa2qEc65fLwiw//kf5+9vq7p47ain4rXui1NYIDHG8VqMCuM99HbzSb0r8uBmK7sGlbqmtOwdOjTUCydN49prg48XF9nduhVYvVoY2SZvhKXPXU1Np9ARY5f0CJukJGDLlk4vFCCIyro694h/q7p47ainuO6LK3gtNpPE9VpsWmtNMYxVaK07pvabXWuVydebGzUK2L7d2LnlaaSmCkIrLQ0YMUIYjZae3ukZEhHXtpM/d+vWCcJGPJ+RvLe2Cmns3h30DLtmLTYrrqMd9RTXfTGB3vabBZJJ4logAbG5AKzdcJlZg7SRKioSPDV2l6ffL3iOxC48wPiivFK7CwqAXbs6f6usFIKjZ84MPkbvYrdmykQhLdcIJKuw45nj59jz8GK1jL04HaPiNWI9diGagatKcwrZjdg1Je3WMtr1o9UdOHSo0E0mjmwDhPmPVq/WNyePma6fSJ9hLwQr21FPcd0XN7BAYryDFypkNWI5dkFtkU+7VkXPywsWEnPmROeeSE0VutXMju6Teh7kcx2Jsza/9Vbn/nV1wggzebyTEtGemiHWBT/DgAUS4xW8XiG7ZW4hO1CbJVrPquhmRJNcSJSXR09wmvUeqC1ZIk9r6NDgAOqWFmDtWn122T01g/RaxbLgZ5gfYYHEeAO7KuRoeaXcMLeQXWjNEq21KnokonfoUG8JTr33b1KSMOxfOkv2tGn6zmFn14/8WuXmeqv8GcYELJAYb2CHBybaXqlYjV2Qiz8l8WL1avJeE5xG7t9u3YATJ4S5jY4fF7rjpNjZfamWnvxa1dV5q/wZxgSOC6SXX34ZAwcORGpqKiZOnIjNmzer7rtixQr4fL6gT6qk8mhra8M999yDgoICdO3aFdnZ2ZgzZw6OHDkSlM7AgQND0lmyZIlteWQswI4GkbsJrEMq/sRrVVkJrFjR+bvVq8lHU3BGKkCM3r+pqcCMGcriSG/3pVmU0lO6VrEq+BnmRxwVSO+++y4WLlyIBx98ENu2bcN5552HGTNm4NixY6rHZGRk4OjRo4HPoUOHAr+dOnUK27Ztw/33349t27bhvffeQ1VVFWbNmhWSziOPPBKUzi9/+Utb8shYiNUVcizHBbmBuXOF1erVYm684gWKRIDIF4aN9P5VEvVWC/3Dh5WXSfHCtWIYKyEHmTBhAt16662B7+3t7ZSdnU2LFy9W3P+NN96gzMxMQ+fYvHkzAaBDhw4Ftg0YMICee+45Q+m0trZSY2Nj4HP48GECQI2NjYbSYVxGWxtRVZXwl9HGSFlVVREJq4wJn6oq++2zC7N5aWsjKioSjikqMnePyctcmub48cL3lhai9HRhW2oqUXOz8fP8SE1NDdXs3Rt6DoaJIRobG3W13455kM6cOYPy8nIUFxcHtiUkJKC4uBhlZWWqx33//fcYMGAAcnJyMHv2bFSKU/Sr0NjYCJ/Ph6ysrKDtS5YsQc+ePTF27Fg89dRT8Id5K1y8eDEyMzMDnxynF3RkrEHvQqRenV7AKox6USLxzrmtvM3mxahnR55vtZFvck9OXV3n7NutrdrXR0/ZsreIYQA42MX27bffor29HX369Ana3qdPH9TX1yseM2zYMCxfvhwffvgh3n77bXR0dGDKlCn46quvFPdvbW3FPffcg2uuuSZotszbb78dK1euxOeff45bbrkFTzzxBO6++25NexctWoTGxsbA5/DhwwZzzHgSr08vYBVGG3uzjawby9tsXowIK6V8q5W5KOoBQezk5gYvQLt7t/L1aW0VZgMfNkxY623PHvXyDffi4PcLx2ul4QXcJsYZdxElj1YIX3/9NQGgDRs2BG2/6667aMKECbrSOHPmDA0ePJjuu+8+xd8uu+wyGjt2bFg32rJlyygpKYlaW1t126/XRcd4nFjqKooEpa4dO3C6vK3uctWbnlK+tcpc+lthIdHmzUL3GiB0t7W0hNoxalTwORS6/mpqaqimpiZ8ngoLO9MoLPRmN5wVXaCMJ3F9F1uvXr2QmJiIhoaGoO0NDQ3o27evrjS6dOmCsWPHBtYPEmlra8OVV16JQ4cO4bPPPgu7VtrEiRPh9/tx8OBBQ3lg4gAO5BaIVreLk+Vth/dKb2C22igxtTKXepfKy4EJEzpn3D51Suh2k1JbG7yOnIiZoO7aWuGcItGcqNNKeBQrEwbHBFJycjIKCwtRWloa2NbR0YHS0lJMnjxZVxrt7e3YtWsX+vXrF9gmiqOamhqsWbMGPXv2DJtORUUFEhIS0Lt3b+MZYWIbjsfoJBrDup0sb7MNphXdNGr5VitzqaCSoyQspfuPGtW5VIsZESpf6qWoyJsvDvzyw4QjSh4tRVauXEkpKSm0YsUK2rNnDy1YsICysrKovr6eiIhuuOEGuvfeewP7P/zww/Tpp5/S/v37qby8nK6++mpKTU2lyspKIhK61WbNmkXnnHMOVVRU0NGjRwOf06dPExHRhg0b6LnnnqOKigrav38/vf3223T22WfTnDlzDNnOXWwM4zHCdXeZ6UaUd3VVVkavq6atTTif2N1VVKR9fmn+VcpCVxeb9NzRzK8d8CjWuERv+23q9ey7777DsmXLsHfvXgDA8OHD8bOf/Qw9evQwlM5VV12Fb775Bg888ADq6+sxZswYlJSUBAK36+rqkJDQ6eT67rvvMH/+fNTX1+Oss85CYWEhNmzYgBEjRgAAvv76a3z00UcAgDFjxgSd6/PPP8dFF12ElJQUrFy5Eg899BBOnz6NQYMG4Y477sDChQvNFAXDeBfp4qmx7hkTu8+2bhW8BmVloXkWvTjyMtEqJ3lX18iR6ulbTVISMGIEsHGjvusoDe4Ggv83e243YeZ+lpcJw0jwEREZOWDdunWYNWsWMjIyUPSje7K8vBwnT57E//7v/+LCCy+0xVC30dTUhMzMTDQ2NoaNcWIY16FHMMQS1dVCbJFIVZW+hjFcOUl/l6I3fa3zWi1edaQpxnPm5+fbeh7Lidb9HE8vFTGM3vbbcAzSrbfeiiuvvBIHDhzAe++9h/feew+1tbW4+uqrceutt0ZkNOMxeIisd4m3AFW75jKSLqsSSVyPFCPB4nqfwWhNn+DUNA3RuJ/dOAUFYyuGBdK+ffvwm9/8BomJiYFtiYmJWLhwYchoMiaG4crC28RbgGpSErB+vbAA7Lp11s5lJO3qsiK4XG9jb+QZ1ErTyhcdp4R3NO7neHupYIwLpHHjxgVij6Ts3bsX5513niVGMR6AKwtvE2+j8/x+YOpUYOZM4a9eMWCknCId5ScKldxcfY29kWdQTUBY/aLjlPCOxv0cby8VjPEYpHfffRd33303fvnLX2LSpEkAgI0bN+Lll1/GkiVLMHz48MC+o0ePttZaFxH3MUjSPv/x4+OjkbUbjm+wD7MxSNFCHkOzfr0wl5HWvWD0GVS6v2Tlsm/1amDQIP0xSEppxvJ9HMt5iyP0tt+GBZJ0VJligj4fiAg+nw/t7e1GkvYUcS+QAK4srCRWg6bdco/oFRNO2WtEwEltBCKzV1Yu+956C0hK0ieQrLxn3XKfMHGBbQLp0KFDuvcdMGCAkaQ9BQskBbiSM4/bPRxmsEv0mb3Pwh3npEg1IuCstlFSLvt+XE1Al0Cy6p61OU9cFzFybBNIjEBMCCQrK5FY9YBEi1jssrRD9Nl5n5mx1+pnKFxaNgvpkGH+WjZZdc9anSe99wiLqLjFtmH+ALB//3788pe/RHFxMYqLi3H77bdj//79po1lHMDq4EwO2o4MM0Gmbp9mQW9Qq5F82HmfGQ3CtfoZ0hPkHc1A4XD5syow2uo86blHeBQuowPDAunTTz/FiBEjsHnzZowePRqjR4/Gpk2bMHLkSHz22Wd22MjYgdUNDY/wiBwjo6C8UMHraUCN5sPO+8xog+/ES4FeG60Qz3ryZ8X6fJEILaV86rlH+IWO0YPRNUzGjBlD99xzT8j2e+65h8aOHWs0Oc/i+bXYzKw7pSdNXtcoOlRVCddO/FRVOW2ROczkwy33mR3PkBVI7SoqMmRX0Fpsbs2fiFY+7Vh3zy245f73MHrbb8MxSKmpqdi1axeGDBkStL26uhqjR49Ga2urhfLNvXAMEmMLeq9JrMQseT0fTi2rUVsrzJekNBWA3pgeBdsNxSA5TaSxS27OmxpWx+B5sQwswLYYpLPPPhsVFRUh2ysqKtC7d2+jyTFOYoV7nLEOI91NZrsl3Ba3FK0JK+3Kd1KS0LjU1kanTKX3SM+enfdKa2tn/vLyOpc9KSpS7mIKd6+J5QVYX0dYdS0i7W71Qv0nLysruwa90E3vMIYF0vz587FgwQL87ne/w/r167F+/XosWbIEt9xyC+bPn2+HjQwTHxit/IxW8F6qEK0UNHbm2+y6aWbzJ71HTp0S/oreNyUb5B0E4nmrq7WXHnFDeYUj1meDVyorK2PwOA4rPEb77jo6OujZZ5+l/v37k8/nI5/PR/3796fnn3+eOjo6TPYIeg/PxyAx7sPuuAg3xi0pxZFEEEOjiJ351pu2NE+FhcLHTP6k6aSnC38LCoJtKClRtkl67LhxncenpxO1tBDRjzFIq1c7X16MellZFYPk5TisCNHbfhsWSFKampqoqakpkiQ8CwskxhbsDMB0Y4Wo1AhY3YjamW+9acvzFEn+xHukpaXzr9QG+XfRJh021NTUUM3evcrHy+9NM/eqG+9Bt2K2rIxclzgN+LZdIDU0NNC6deto3bp1dOzYMbPJeJa4FEhx+jB5Dq3rZOU1tCItpUbAiRGWkeRFz7HSPI0a1elBsit/SjbJPXMKNgRGsSmlJz1WKsLMeMGiVY94vc4yar/V3tcYxTaB1NTURNdffz0lJiYGutiSkpLouuuuo5MnT5o22GvEnUDiB88bROs6WXketcY8mo2oKBYKC+07Z0uLII7E81RWRv85kparQhnX7N0rdLHJ7ZJ7n5S68dwmRuKxzuIuTF3obb8NB2nffPPN2LRpE1atWoWTJ0/i5MmT+Pjjj7F161bccsstVoZHMW4imgF9bhtp5SXUrpPVZWrl/aAUbB7NEUbV1UB5ufB/eXlnOVl9D9bVAbt3d54nKcmaGdON2CotV3kZ+/3AT38KXHJJZ1CwmHZubnBw8LRpwd9zc903AMCNQch21208Ya+1GFVe6enptH79+pDt69ato/T0dKPJeZa49iDZGTsQj299VhKuy8qqMo2lWJLKyuC37h07IisvNU+KmTILd+2Uur7MenGqqqgGoBqxHCorgz1r8rSl+XSj5yLa96iRySnt9u66yZPnQmzrYsvJyaGdO3eGbN+xYwf179/faHKeJe4EElF0Hjw3VrReQ36d7CrTWKmIpV1sRUWhgslIeekRNEbKLNy1k/8uduGZFHY1I0cKAqmoiGjnzuC0Kyu18210ZF407h+z57Aj9ofrNtdgWxfbfffdh4ULF6K+vj6wrb6+HnfddRfuv/9+yzxbjAuJRrcHu4gjR36d7CpTL0y0p4ekJGDjRmE+nbIyIU9myytct47RMgt37aS/FxR0duFF2qVEBLS1mT82HGbnjzKKmXvUzFxNerrzuG7zHIaXGhk7diz27duH06dPIzc3FwBQV1eHlJSUkOVHtm3bZp2lLiMmlhpxK3E6/b2tuLVMY80uO5ZOCWeLdOmRqVNDz603L9XV2Pfj0h35ADByJFBZKfxWWCiISLXjjS77YWQ5FCuX1tBDONuUylN+3detU14Gxq33e5yht/02LJAefvhh3fs++OCDRpL2FCyQGCZCnGj8zGC0UXPT+mxGytjvx76xY4Hdu5FfUADs2tX5W2UlMGKE9vknTRKCz/VcS71CMtL11sygZZtWeSoJVTff13GM7vY7Ct19MUlcxiAxjJVEMyYjklgUtw4aEPOkNSeRPJ5KK46IJMP81Sab1LJFjEEaN07fFAZG54+K5mAANdv03LMca+R6bItBYhiGsYRoxWREsv6XG4eKA8F5Gj8+1Ea/H9izB7jhhuDj5swJvwjyoEFAaqqxdc5qazunSti2Teie07Pgcrj4IDvWW9MT06Rmm557Vs9iwYwnYIHEMF7Hq/NGRWux0UhEjlsDa6V52r0bGDVK+F86J9HIkYJYkVJerj//SUlCfkXBpYW0nESsEJRq8T5m7/dIF8s1es8ai2DRxqvPuYdhgcQEE42HUOscVqx4Hk9oVfheKD+3j4y0QsRZfR3EZ0P0UowfD2zZ0mljXV2neBJJT+/cV2/+jYgJsZwqK4PtikRQKp0/UoFjhUcw3D0r9aYZEaRaRJpvxhxR6vKLOWIyBika8RZa55D+FsmK5/GE1orfbo2dcQKn5myy+jrInxGlWB/5OSsrtSeQlJRNYC02IvOxNFaVtdULGbe1BU9+aVdMkx1xUxzXZCm2TRT58MMP0w8//BCy/dSpU/Twww8bTc6zxKRAisZDqHUOK1c8jxfUKuNYqVC9Phml1ddBb3p6y00mpmr27u0USFoNfbQmeTSzkLHa2n7hhKXVtltZPlaLLq8/VxFim0BKSEighoaGkO3ffvstJSQkGE3Os8SkQIrGiJFwlW6Y1cYtOX+sVQzhGgSvLgVilffFyWtu9XVoaSFKTxfSS08XviudU29+ZYKr5pNPOgWSWlrR9E6qnV/LG6ZkWyy8MFh1H7N32T6B5PP56NixYyHbS0tLqVevXkaT8ywxKZCInJ/+X/qbnW9hahVDNBtTu8/lRTEotdmKRs0NjYGV1yFcmRjNr3R4PiAsNbJ3rzkbjOazpYWopERZ5JlFbltJiX7PU7wQC2IxQiwXSFlZWXTWWWdRQkJC4H/xk5GRQQkJCfTf//3fERvuFWJWIMUyVjcukeCGhtttyMtEay4evY2x1xqDcPlqaQleb03eVWQmv5K5kmoAYR6kcDZGuiCyHk+Y0nmNzJskpi/a48UXBjtgsWi9QFqxYgW98cYb5PP56Pe//z2tWLEi8PnLX/5CGzZsiNhoL8ECyYOEqxii2Zh6reGOBkplEmkXjx2NgV0Nbbh8SX8fOVJ5EIOZ/EqOqSkoUPcgaXl3jd7PJSWhnp5Iyka+rzx9fr6CscM77yHxaVsX2xdffEFtHikEO2GB5FH0xi/Y/WbFb3Gh6C0To42xlZW3nZ6/cPkKN4hBzKfWiDW1PFVWElVWBgdpy/fRK9703M9GPUhmrrlXni87BXc0RIsHveG2CaRVq1ZRiYLaLykpoU8++cRocp6FBVKMEksxSF7EaDeKkcbPivK20/MXLl/yhkg6iEFruRG95ywsFIK0lTxIevJtZwySWc/Yj8LPtc+YlriI5H6NpmjxoDfcNoFUUFBAq1atCtn+97//nUaPHm00Oc/CAolhHCASL4lVI+Ls9EyEaxTVurnMNlLyUWwA1YwaFZq+GzwyRgWDlSLBrpcZPfOYmZmWIJqiRc+94bKXQdsEUmpqKh04cCBk+4EDByg9Pd1ocp7FUwLJZTcnw5gikgbPygbDjc9TJF418ThRIAFCgywvazfmWwurrrnSfWdVWUjTLiggam4W0pUvMhyJZzAaglZv6IJLuuBsW6w2MzMTtQpTp+/btw9du3Y1PJM3YzM8RT0TK7hlTTVxqQnAPUu5qC2JEm6ZE6UlQgoKhL/yso7GsjBWoveahysj+X1XXW1dnZqUBKxfL6ylt2sX0KePkO6cOZ3XQ8TIPR+tdQ6l51O7N9y64LMejCqvBQsWUEFBAe3bty+wraamhkaPHk3z5s0zrOReeuklGjBgAKWkpNCECRNo06ZNqvu+8cYbBCDok5KSErRPR0cH3X///dS3b19KTU2l6dOnU3V1ddA+x48fp2uvvZa6d+9OmZmZ9LOf/Yyam5sN2e0ZD5IH+4djEivfOL30Fm8lkb4VeyVY2yrkUwKINqqVQ1sb1axeLcQgRcsDYcU10UojXJyTnusoLwu5dyfSQQJqwfdi/JQYa1ZQoH/OKDfVE27onpVhWxfbyZMnadKkSZSUlEQDBw6kgQMHUlJSEl188cX03XffGUpr5cqVlJycTMuXL6fKykqaP38+ZWVlKc7UTSQIpIyMDDp69GjgU19fH7TPkiVLKDMzkz744APasWMHzZo1iwYNGkQtkhtr5syZdN5559HGjRtp/fr1lJ+fT9dcc40h2z0jkNx2c7rpwY0WdsS/uLVRthu33D9uf/Foa+sUR/KRbhqxLUFrsdld1lbcz+GCnMOlr/c6SsvCyAg8owJMTFdaV6sJ3UjOaRaz94RbntsfsU0gEQlemk8//ZSefPJJevHFF2nt2rWmjJwwYQLdeuutge/t7e2UnZ1NixcvVtz/jTfeoMzMTE27+vbtS0899VRg28mTJyklJYXeeecdIiLas2cPAaAtW7YE9vn73/9OPp+Pvv76a9W0W1tbqbGxMfA5fPgwAaAqyQP1zTffUE1NDYnTILS1CYs/fvPNN4F9jhw5EjSM9tSpU1RTU0MnTpwIbDt8+DDV1tYGvjc3N1NNTU2Ql6u2tpYOHz4c+H7ixAmqqamhU6dOBbbV1NTQkSNHAjfnN0ePOmvf3r10aty4wINbs3evYJ/byy9S+1avphOSSvjw2rXm7Fu9mk5JA2pXr46P8nOjfT82QqcgzB10QrK6gKP21dUJEz3+6OU49WNM0YkRIwL1wGGAaiX3UfPYsVSzd2/AzpqaGvvt+9GWgH0ScWKo/H4UOCd+TOfUjh2d9q1eTUck+fymrCzUvr176ZvzzguIkiN1deHvv7Vrg8tv2zb99pWXK5dfW5tg3+7d1PajaA2UX1lZ4FxHEDyRZ8T2Gbm+R49SzahR1PZj/d3W0uLZ59e2GCQA8Pl8uOSSS3DXXXfhtttuw4UXXmg4jTNnzqC8vBzFxcWBbQkJCSguLkZZWZnqcd9//z0GDBiAnJwczJ49G5WVlYHfDhw4gPr6+qA0MzMzMXHixECaZWVlyMrKQpHYNw2guLgYCQkJ2LRpk+p5Fy9ejMzMzMAnJyfHcJ4dwy2xA4cPA9u2Cf9v3Sp8jwdycoAxY4T/x48HcnPNpzNuXGc6XroHYw0xxmPHDuB//sf5ZwsQYmH+8z+BSy4JjmEZOhRYs0awMS8PGD06+Ljt26PzLErtmzxZeA7E+3nsWO24ML8fqKkJjfeRxhkVFACDBnXGFGVnd+a1qAgYODA03aQkoKTEWKxObm5nnNb48cI51cjLC44luvlm5ZilpCQgPx9ISQmtqwcO7MzjeeeFf+712hcu9krOwYPA7t3C/16LJTKJj4go3E4vvPACFixYgNTUVLzwwgua+95+++26TnzkyBH0798fGzZswOTJkwPb7777bqxdu1ZRrJSVlaGmpgajR49GY2Mjnn76aaxbtw6VlZU455xzsGHDBpx//vk4cuQI+vXrFzjuyiuvhM/nw7vvvosnnngCb775JqqqqoLS7t27Nx5++GH84he/ULT39OnTOH36dOB7U1MTcnJy0NjYiIyMDF15jnvEgPGtW4UHNxrBg5Hi9wsVQV5eZLa6LR0m9qiuFgJ8RSorO0WR9F4RG8Y5c4Dy8qBncd++fQCA/Pz86NgnovUCJ603iooEYZqU1Pks5OYCdXWdAkvct7BQ8KFs2yb8/+WXnfuZfXaUzqmVlt8vvBxJ81pV1Rnkb/S84vnC1QN6flcq03A2eK3+VqGpqQmZmZnh229N/9KPDBw4kL799tvA/2qfQYMG6UmOiIi+/vprAhCyRMldd91FEyZM0JXGmTNnaPDgwXTfffcREdE///lPAhDkdiMi+ulPf0pXXnklERE9/vjjNHTo0JC0zj77bHrllVd02++ZGCS34bK+aE2s7sv3Ut7jHS9eK6Pxhm1tIRMpBsUg2WmfdKLLcM+W2hI0Ss+m1mzjRuJ4pDYrzQWlNw15QLc4x5RS+nqxol4yG0PnxedCAUu72A4cOICePXsG/lf7KA3/V6NXr15ITExEQ0ND0PaGhgb07dtXVxpdunTB2LFjA2894nFaafbt2xfHjh0L+t3v9+PEiRO6z8tEgFu6+/Rg5fBUnm7BO3j1WmkN7VbrTpk7Fxg5sjOffj9w4IA9eZba9+abgvcKEJ6t0lLlc4o2id1U4nB9tWdT2uVWVBQ8dYHR7iH5fVBdbaw+8PsFL52UlBTl9CdNAvbs0VfuRu1Qwuy0F16qv63AiOo6c+YM5eXl0Z49eyJSbyITJkyg2267LfC9vb2d+vfvrxqkLcfv99OwYcPojjvuIKLOIO2nn346sE9jY6NikPbWrVsD+3z66adhg7TlsAcphtAY9mzZCEC3j3piOomVayXe12rLkMjzWVlJNaNGCRNF2j1KUmnkltYoNPmIO61nU+71kZeB3mdZoXwMpaHmzRLvJ6Xf9SzEK3rerBgVGwPeIDPYNootOzvbMoG0cuVKSklJoRUrVtCePXtowYIFlJWVFRi6f8MNN9C9994b2P/hhx+mTz/9lPbv30/l5eV09dVXU2pqKlVWVgb2WbJkCWVlZdGHH35IO3fupNmzZysO8x87dixt2rSJvvzySxoyZEjsDvNntAnnrraqErFjugWnKjit88ZCpeu2qTHMIM2D0nB/+T4/zu8TmEnbLmEoFy8lJepiNJxQlYofPfdcuHtT/rvSfWB2/Til4fvS3/UKciXRxhjGNoH0+OOP09y5cwND6SLlxRdfpNzcXEpOTqYJEybQxo0bA79NmzaN5s6dG/j+61//OrBvnz596NJLL6Vt27YFpSdOFNmnTx9KSUmh6dOnBw3FJxImirzmmmuoW7dulJGRQTfddFPsThTJaBPtNYuU1tEym5YTcyJpndcpm+zAzUJPj23y+1oUSWG8LQEPkl3rzCkt2aHlCQq3eK90IsWiIuNr9GnZJm6PJAYpnIiT50FceFjPsh1eFe9SHHrObBNIl19+OXXv3p369etHl1xyCf2f//N/gj7xAgukGMGJCsfJIEslW4xUUFrnjZWuKTej996R39c6hUPN3r2d8xRZjdbCrEY9kmreFzOB2Fq26d3Hihcere5QpX2tvEZKadotXhx8obJtHqSsrCxcccUVmDFjBrKzs4PmBsrMzLQwOophokC01ywCrAn+tmJtMTPByFrntXK9M0YZvfeO/L5OTe0MrvX7hYBgpaDgpCRh3hw7ngO1+0Mr8FftN2k5iMgDsaur1ef5kQet67l31faxIqhfzGddXfjrq1VeWtdWDSX7ozFQwQtrtEVJsMUc7EEygZu7LaKJVV6rSMvTjqG+fI3tJdJ7Rx7kW1gYlIatw/zF81sd01dUJHRTyb0vatMIhOtOC9fFJf/NSs9pJNc3zLVVRW5/SYnx9ebMILXXpR4kwwJJbc21xsZGuvjii40m51lYIBkkluJTrMANIiPW4hnihUjuD6WRU5LGz3aBZCVa3UJaDXy4rjJpcLs0GFtPd58o1iJ5lsxe3zDXVvN88mDywsLguCg76oaWFqKRI42JOYuwTSD5fD7FxWQbGhooKSnJaHKehQWSQTg+RR/RFpLREGN2noM9VsaQexlk95inBJIWeoO/CwqCRZBScHtbW/gFY+XB1goLAZvOh977O8y1DXusfERhZaW9z63a6MooYHkM0s6dO7Fz504AwJ49ewLfd+7cie3bt2PZsmXo37+/DZ2ATEzA8Sn6iHa/vN0Tv9kZy+DVCR2dJCkJ2LhRWPqisjL8EhNqE0waXcfLCoycUyu2MCkJWL8eGDUK2LULmDo1OBZp1KjOfXfvFs45fnz4iSYPH+6c/LK8PHgCTrP5NXJ/G7228mOnTw+uo4cOta9uqK3tLE9AiB9zY5ugV3H5fD5KSEighIQE8vl8IZ/09HRatmxZxMrOK7AHyQT8th+eWOv2stNzaHXa0RodFC30xNTICPIgacXqRLu73Opzas0nJPcWrVoVvG9BgbpHSuyisuK+dMLr7kT3vtyLFwUs72I7ePAgHThwgHw+H23ZsoUOHjwY+Bw5coT8fn/ERnsJFkiMbcSSkLRT8FmZtrwBNjufjlaa0byeSo22DhuCBJJaAx2NAF45dohhrYBmeZeZWIZKjbnctlWrrInfkd/fVtyTbsLBes62GCRGIC4EUiw11IxzeCEGSW1ixUiEjZNxd+GWuVBB1YMkNvSRxLlEgh1CO5zQUxrdpTaoQqmcrBqtp3d+JLVjuf4OwbZ5kN58802sWrUq8P3uu+9GVlYWpkyZgkOHDlnU8cc4Dsd3MF7AqhgqaYycmYVNw6WpJ+7OyrievLzOhVpFiopCbZCfU7pYrVIcT21tZ5wNICw6G425w+yYr2zoUO3rI79+06erz9Ukt82q+9LI/EhSolV/OxGLFk2MKq+hQ4dSaWkpERFt2LCB0tLS6LXXXqPLLruMZ9KOJXjUGWMFXpreQelt3YquO73rhFlRTtLzyT0k8nW7FLoVwy5Wa3eXqVGPR6ReknDHm01fz3FGR6hFslCueB69NunpzvPSsy3Dti62tLQ0OnToEBER3X333XTDDTcQEdHu3bupV69eJkz1JjEvkGItWJhxBq8K7Wh3T1hRTkpxVFrPsEIXkq7Fau0KZje6xIadDXQkedSbFzu7zJTil8Ityq0Uc6Vlm5571qXdfLYJpLPPPjuwQOyYMWPorbfeIiKiffv2UdeuXU2Y6k1iXiARqd/cLr3pGRfCQlsfVpSTUa+B/JzNzVQzZIj6YrV2enjCNbZKgsLqGaxFOyMVXmp2Sc8RjRcHveeT5tdI3JrWPSsXXC7zMNkmkK699loaN24czZs3j9LT0+nbb78lIqIPP/yQRo4cac5aDxIXAkkJD7tVGYdwq6A20p0g3d+ufIQTM0a6PPSKLFm3Yg1ANUOGhI7UMuvxkB+j9dJlxNslpqEnv3q60aR27tgRmXiRThMgDdo24t2zGq2yUgroFz1I4WyTC0t5F3U40Rtro9i+++47uvXWW2nWrFn097//PbD9gQceoMcee8y4pR4lbgWSV7tMGEaKtMHQ053g5IuBkXOb9fL8OItyoIst3IguPc+90lxD4bp59Hq7pN1sWl5uta4lLe+KuPyFmWsttVO6VIkZ71605uSS31/iunZGzi/Pt1wcac1m7sCLNg/zt5m4FUhe6jJxq+eCcR6jw+DdNGTfynPLhGINQDXyiRDl+xnxTkmPiXT+JKXnWW2b2LUjFTtKnie5N6egIHj/nTuN2ajVvaa3/JwQD5HWlVrTZCgtueLwi7alAmnHjh3U3t4e+F/rEy/ErUAiMv4wOSFUuCuQ0ULJgxSum8apFwM7zy1rqGqWL6eavXvV7TDq8VCL7bEiH2reGrkQE0WSeE4tb05Li/YEkkZsUvKa6Ck/t3rpjXj4wnmgHH7RtlQgSReoFZcckS4zIn5PSEiI3HKPENcCiUi/6HFKqLi1klGCPV3OIG0UnYxB0pOuneeWNFQ1e/caX6zWyDPe0iJ050WytERbmyCC5EuAiAvLygXSjh3BZacUIyRFbXoEI3We2e4pJ+KTzNioJIQrK40t0BsrMUgHDx6kjo6OwP9an3ghrgWSkQrRKaHila7AWPN0sdgzhhuuv+SaBc2krRe9z7ieRjach0Xu4UlNDRUz0i42+XnUvE7yc8m9SGZmstaLwnQLrltWRO8oOD1l44I6gmOQbCauBZIR0eN014SbKhklvOTpCocbGnuv4bLrryqQwokXLUGip+tIvkCsmIbomRDTVwoAzs8P7RJTs1dvecu9SD8GsasKhEjqGaXuXrc9P1r1uNn2wME86m2/dc2D/tFHH+memXvWrFm692VswO8XpqHPy7NvCQBxCv6tW8MvoSBOw2+lTXrzKE7T72aMlKXbqa0NXQ7B7eXvNF64/uKyFVu3CraWlak/d0Tqx61fr5xXv1/4Ll/eJS+v83iR3buBkSOBykrhe1ERsGwZcN55wvfycqC0VFgWROneE5dgKS9XXnpFRFyGRLR12jR12/WWjRpiHVlaCsycGVwGbnl+tOpx6T1cVCSUibhUjRyv1RF61JY03kgtBkn8xAuu9CBFU5075Z1xyRuIpXjB06UHK72FsVImenBRXhU9SOE8BGq/6x3aLt9PHEWnNNKwoICouVnw8GzbJsQhNTd33ndpadp1g9TbVVioHTMjt1WP7fLlXIwQbW+72ftO6Ti5p0/PDOIOhj7Y1sX22Wef0bhx46ikpIQaGxupsbGRSkpKqKioiFavXm3aYK9hm0CKpLJ0mbveFuIhj17GisY+FkWwR1AUSOEaNbXf9TaG0v0KCjrjgqTbCws7R6MVFQmiSOyOSk8n+u47oiFDwtcNSqJLqzHXEzwvjVcaN84bLwZmnzG149ratLsh5WnEagzSyJEjaf369SHb161bR+eee67R5DyLLQIp0oZBKxYgVnDJGwhjIyyCHcNUDJLW73obw3DHy2OCHnss+Psf/6jshZKnJfVyaN1j8rpYLWi6rY1oxAjrvEhWYzQWK9z1UvMKak2Z4QJBJEdv+51gtEtu//79yMrKCtmemZmJgwcPRtTdF/co9c+aRRoLEA6/H6iuFv66HbEvvKoK2LDBvjgrxjnEmAbAvXE5sYrfDxw4EFoXiPF8as+b2u/hjtN7vBgTBADp6cB99wE+X+f3P/yh85gBA4B164LTEmOFRo4Uvu/cKcQiAcr3mLwuHj8eGDZMSENaNrW1wJ492nlzCjHPSnZLn7GCAiA3N3j/sWOB1tbgtKqrhf3E48R4o+rqzrI6dQooKemsm7Vs8AJGldfUqVPp3//936m+vj6wrb6+ni655BK68MILjUs5j2K7B8mMd8TMm3c8xC0x3oPvlejT1kY1o0YJS42EW3bFimtjNB2lbpzXXw9dO030ZEiH8OuNh5KeS212bfkItkgmljRbFnoI1x7IRw7KvXTivFJK3jSpJ66wsPP/SEa4RRHbuthqampo1KhRlJycTIMHD6bBgwdTcnIyjRw50vj8GR7GlTFIZgSWWVdrJLbFavcfw3iZqqrOtdjUGjOrnmOldPRMIqlUx0m3ST8lJdrH6bFRnHspXAyWfJJEI/WnPHDcjjpXzwLAlZWh0yhUVSm3EfJt8sk4tWxwwcuPrfMgdXR00Keffkq///3v6fe//z2tXr06MJFkvODKUWxE5t7KtCocq8SMS98kGIb5EakHSU1EhHuO9dY/Sg2sNOg6nEhSGkUVLo1IXz71znZudFJJuQfMyhgmvV4y8XorzTKu1kbo9ZxJy0XPSLcowBNF2oxrBZIZ5A+RHWKGg6sZxvXU7N1LNatXq7/paz3HRl6s5OnIlw2Ren+MYMVSJkZQEkVKXhjpvtKybWkJXVA3mkHeatdYzzYjiw+refkcelFmgWQzMSWQ5NglZpQqCBZKDOMaAqPYtMSO3pFRlZX6R761tOj3IGmlo/a70XXC9J5XTRRpeWGkMTtKxyl1U9lZV8rT13u+cGJZ66Vbfl9FuT1ggWQzMS2QiOy9YTkeiWFcSUAgWTHgw2hXSkuL4EnascNciIDa+m5WBFErIS8jqSiSTwugJBDkx0nngNKbP6MoiSG1hXLNrKsmilH5tW9rCxWD0kWAo9wesECymZgSSNH25nA8EsO4EkUPkhEvsliXGOl+kR5rNFjZ6CzfkdQ3WuJCSRTJj1USj+GOs7KuVCpfefp6J3tUS1+pG23HDiHd774LHTWnZEMU2gNbBFJbWxu9+eabQUP845WYEUhOeHM4HolhXEnQRJGRBjXrfcbF85gJVlYKKpanLfUgma3j1OpJI91R0m4+s91YWmIqHHLRKtoiT99s3awkRgsLg7tOxWVipB6mSM5pEts8SGlpaXTw4EHThsUKMSOQnPLmcAwSw7gO1Zm0ldAT+6Nn5JfYOBoNVpbHALW0qAcYRxqDFEk9qeclVK2spLY3Nwd7YPSIJWm6SgJJ6dxm62Z5PisrlYPvjc5JZQO2CaRp06bRBx98YNqwWCFmBBJ7cxivweLaODrLTLdAssrzLG8speuthUtTKSjcyCi6SKdDMZvHkpJQAafmnRI9LePGhQpIqVjS063X0mL/EHt5uSoF34fz+kUB2wTSu+++S3l5efTiiy/Shg0baMeOHUGfeCFmBBIRNziMPtxwn3CAv3EMlJlugWSV5zmSLiR5vuTeCjWb5DNIG42vMuoxk4oEny/0vGrxWvLt0s/QoeHz6gJPDbW1CV2nq1Z1evjkXj8HsE0g+Xy+kE9CQkLgb7wQUwKJsRYnhYRd5zbbqFgNB/gbx0CZ6V6sVsujYnQuImnaRmJzlCYfVFosVX6c3BOjVh5GniUtEao2gk1MWy1GSi6QRLsLCoTutnAeLad7B5TKxCXPr20C6eDBg5qfeIEFEqOIkx4Ou87d1qY++V20cbrS9yIGykxRIBkJUI50PiN5l1C45SuU1hBbtUo93ki+b36+so3hniV53rUafmlacgGn1E0oPU4ezGw0Vsiq+CIzqHmwXPD88jB/m2GB5BLc0O0jxck3JLvOLU+3oMD5bjY3XXMvoFVmkt8UBZKR+0o+TNzIjNhq8wqF88jIY4/U4mza2kK74QDlKQX0Ch5pcLjWpIlioLWSyAm3zptRj5reqQbC3Au6CHdfKeXNaJ5seM5tFUhvvfUWTZkyhfr16xfwGj333HOmgrdfeuklGjBgAKWkpNCECRNo06ZNuo575513CADNnj07aDsAxc+TTz4Z2GfAgAEhvy9evNiQ3SyQXIAb41GcfEOy69zSdJUms2O8i+wZqtm7V9uDFK4Rj8SDJD02NVWfQJEvgKoWzyP1xsjTlu8nCg21PCsJObXuQTtEifwYJQ+V0rnCCV2j9aldeTNji0FsE0ivvPIK9erVix577DFKS0uj/fv3ExHRG2+8QRdddJGhtFauXEnJycm0fPlyqqyspPnz51NWVhY1NDRoHnfgwAHq378/TZ06NUQgHT16NOizfPly8vl8ATuJBIH0yCOPBO33/fffG7KdBZILcEl/dghOejjsOjd7bWIT2TNUs3q1vhgk6XZ5t5g0KDcc0nTVPEhGPCtqAk0unAYP7vxfOtuzdDmQnTuVu+rUupyVbLKjjgrXxagmKqWxWkplatRWO+tfm+t22wTS8OHD6f333yciom7dugWEx65du6hnz56G0powYQLdeuutge/t7e2UnZ2t6c3x+/00ZcoUev3112nu3LkhAknO7Nmz6d/+7d+Ctg0YMICee+45Q7bKiYpA4kZJG5f0ZzMug58b/cieoZrduzsXq9WD3m6xcOcWBYneGab12qM2ImzHjtC5kZSCqaXiST58XTolgXyJDtH25mbzHjW9eZR2MSoFqcvLWS0+Sx7zZOT6WV3/2ly32yaQUlNTA91qUoFUXV1NqamputM5ffo0JSYmBsSWyJw5c2jWrFmqxz3wwAN0+eWXExGFFUj19fWUlJREf/7zn4O2DxgwgPr06UM9evSgMWPG0JNPPkltYS5Aa2srNTY2Bj6HDx+2VyC5sfvIDHY3VnJXczw2jG7Lt9MetFh4bqKJpFupZtQoqjFSdvLu13Bv/Voeo3ANuN68qMW9jBvXeY5wAkFNgEgFk5jeuHHqI87sGNyg1cWoJCrDxVNJ60/5UiThnmU7n3UvxiANHz48EGskFUgvvPACjR07Vnc6X3/9NQGgDRs2BG2/6667aMKECYrHrF+/nvr370/ffPMNEYUXSL/73e/orLPOohaZan/mmWfo888/px07dtAf/vAHysrKojvuuEPT3gcffJCUYptsE0hu7T4yQjQbq3htGN2W72jZo1Z5xsJz4xRVVVQDCALJSNnpidsR95N7WcTvVl4ztVigcOu8KcUpjR+vHNek5JFSEldSkWTGE6LVlahnKgWtrrVwXXVGJt70GLYJpKVLl1L//v1p5cqV1LVrV3rnnXfoscceC/yvF6MCqampiQYOHEiffPJJYFs4gTRs2DC67bbbwtqybNkySkpKotbWVtV9HPUgebX7KJqNVbw2jG7LdzTs0RJhsfDcOEVbW6cHyWzZab31qw37Dhcboydt+W/y73ruS/k+Q4aoj1BTEhNKsUDinEVWBirrfQkJ17Wm1VWnJgyN4jbv9o/YOort7bffpvz8/MBEkf3796fXX3/dUBpGu9i2b99OACgxMTHwEc+fmJhI+/btC9p/3bp1BIAqKirC2rJ7924CQP/617902++aGCSX3oBEFN3GSs+53FxWZnGbIIiGPXpG48TadY4SNXv3GotBMoLWvaGnK0ePKFaKBxK7ivTUD2pdYkoCTB6vIxckRpZNUULtPtf7EmJk1Jq8qy5cmeltm8LFPjn0rEZlHqQffvgh7IgzLSZMmBDk4Wlvb6f+/fsrBmm3tLTQrl27gj5iAPauXbvo9OnTQfvPnTuXCgsLddnx9ttvU0JCAp04cUK37a4Yxea27hUlovkAhHvDdHtZmcVtgsBue9wmCmMIQ4vVmsHsvaHV2Mt/k8/FpDSEX82G5maitDThOKWgarmAUOrKCzfdgN78K4kwcbue+1/Pmmd6hKk8mF1PdyWReoyZUU+YDdgukBoaGmjdunW0bt06OnbsmKk0Vq5cSSkpKbRixQras2cPLViwgLKysqi+vp6IiG644Qa69957VY9X62JrbGyk9PR0+sMf/hDy24YNG+i5556jiooK2r9/P7399tt09tln05w5cwzZ7gqB5LbuFTfDZRVbuE0UuhmlRk4F2wWSWdTEgvibVDBoxUKFa5TDBTQreabCTX8g9c7IJ5jUm2e5EDHicZNOZmn0mVHKs1L3YrhjlfZX6uLTeZ9Gim0Cqampia6//vqgLq6kpCS67rrr6OTJk4YNffHFFyk3N5eSk5NpwoQJtHHjxsBv06ZNo7lz56oeqyaQXnvtNUpLS1O0p7y8nCZOnEiZmZmUmppKw4cPpyeeeEIz/kgJVwgkfpPWT6yXFQsGRgnpcPRwb/xkQCCZbWzN3qPhvBZy75Cal0ipUZano3eCyHBBzPL8Kk2JoFUWel/qlLr/5F40NVvDXRMlG/QKJDF9+ezl4v5y8SWOCtRxn0aKbQLpyiuvpCFDhlBJSUkgYLmkpISGDRtGV111lWmDvYYrBBIRN4xGiEZZOXE9Yrn7kDGPUkxNGO+pLoFk5n4zs9ix9FnSIxb02CUVWnrFllrXltEgZqXroXWMnpc6eZ6l3jPpnEhKtuotLyVPWLj5kuRdkVrePyXRZbOX3zaBlJ6eTuvXrw/Zvm7dOkpPTzeanGdxjUCyAhZZ1uCUUOHuQ0YJpRiQMPelLoFktzAQj1Fr+NXEgt75fvTYrybo5A2/Ua+0PC4o3GSYRj08SmvhyW0Vlwsy66EKZ5eZ7j25cLW5/rRNIOXk5NDOnTtDtu/YsYP69+9vNDnPEjMCSXozRzpJW7xjt1DRincwMgMuEx/IG6odOyKPQdJqyNTuT/lzoWexYzPxKWqCxajYMiLojIgHpW7ASF+o5MKnuVk9b3LRp0d0hsuTEka7EqXn8HoM0muvvUbFxcV09OjRwLajR4/SJZdcQq+++qpxSz1KzAgkE2+ZYYlXj5SZN0ozaSu5qfWMKmHiD4PPYliBpBbDE+7+lHsv1GyVrnovvaf1vgAo5VfpxUVrokUzgk5EKkJGjhSWT5F7cMQ8WPVCpSR89AhVafdluDI1Ur+Y8RhGGdsE0pgxY6hbt27UpUsXGjx4MA0ePJi6dOlC3bp1o7FjxwZ9YpmYEUjSB9eKGzre42HsEodGhjm7rDJivIMuD5LSS0Ckc1PJPVPjxnV+lwaZ6+2e04odCue5aWnpHOqfkiJ4ZfSgJAyknnklcWLFC5WRrjIz5zMSlC2iZ4oBB9HbfifBIJdffrnRQxir8fuB2logLw9IMnwJg0lKAsrKgOpqYM4coLwcGD9eSNsMtbXA1q3C/1u3Ct+HDo3MRi+RlGRPfvPygKIioUzl10frN4bRi98PHDgA5OSo7yPWF/L6J9w9GO65qK0V6h6Rbds6/6+sBEaNAnbvDn9/+/3A5MmCHUVFgq1ym8PVUdXVQEuL8P/p00BdHTBihPo5pXnYvTt0u5gvefkolaWZul1a9kVFQhp+f+jxatdOjh4bwu2Tmgps325dO+UUURJsMYdjHiQ7PTRWeD/s7GYyYkMsdvGFC4yMxTwz0eHH57YGoBq9MSNKaRi9P6VxOfLRZdLlR8IFM4vo8WTJlzaRpt3WFuqx0uMxEdMW6z5xLTdpt5e0+zDc3ElG63Z5vqyIaRIDrFtaOkfEpacHxzl5tJcgKjNpxzOOCSQvdKc42VjHexdfpLDQik9+rFcCi9VaWa+oPZPy7c3Nwpw50ridSCc2lM8ALR+QIu9uk3cnGRWLUsEnFUTS86rNjq02A7helEa0GX2OlQKsd+4M3vbHP7q/DQoDCySbcYUHyYV9u47jBQHpVlhcxi56YoBED5KRoGQ9qD2TSo2x2r1nRCypeVOU7JBv27FDX0B5JPnXikWSzl1k9BoopWPGEyWPo5JP9BipnS6ABZLNOBqkbfdbvpe9CCwgzcPiMjbRK3zb2qhm9Wqq2bvXvvOrDb8vKFC/9wzYrzmxpJId8uDwwkL93Xlm8l9UFNy9p2TrqlXmh7pb4Ylqbu7sIhS71KRlFImHyiWwQLIZ149iMytyYsGL4GWB5yQsLmMTA8LXtrXY9MQg6V3iQ88s2mrpKdlhdOJLM3mXzu+jNcpOKqDMetIifY7VRtxJvXJWedccImoCye/30/bt2+nEiRORJuUpXCmQlCoboyKHvQjxDYvL2MNAg2mZQDIbPxQueNnILNp6uhXDibNIMeL9qqwM7c4y40mT5s1oXlpaiEaMCO5KkwohM0vGuBDbBNKvfvUrev3114lIEEfnn38++Xw+6tq1K33++eemjPUirhNI8tEHZkWOvDKy2t3MMEz0CefB+XG7JQJJKyjbrGhqaRHig1atUvZcGPWaSLvW7OhWEzEzR5E0vkdqVzRm6leax0k6ii9GXqBtE0j9+/enLVu2EBHR+++/T9nZ2VRVVUX33XcfTZkyxZy1HsR1Akkt4NHoG5HUHWzFVPgMwziDzsBs6fOtKpCU0lJLP1z8j966RHrMuHHBQ83VRJJekWNm8sNwtiqdWz5EXq1bSmkEmrz+tdPTpWSDUtnESDe8bQIpJSWFDh8+TERE8+fPp1/96ldERFRbW0vdu3c3bqlHcZ1AssLzI6/E7O6bZxjGHvQIEoXnW1EgKaWl5SWSzzNk1vuhNfpr1apQG6VzGYWr+8IJJCNiS6vbyews1+JSKEa7DSNBaoP4SUsLFXUx0A1vm0DKzc2lTz/9lPx+P+Xk5NDHH39MRES7d++mrKwsc9Z6ENcJJKLIb1z5w1xZGRNvCwwTd+iZMFFh0VlFgaRneLzcSyRf+NqM50GenuiJET1KSmnrXbdNmn+tteP0xPpohTTo9SCJacljoqI9nL6tLfJRcB7ANoH04IMPUmZmJp177rmUm5tLra2tRES0bNkymjRpkjlrPYgrBVKkKFViMfC2wDBxRzhBovQyRCoxSGr1gnybHlEWSeD2jh3KXp9w8wzpSVurbLTSkO8rn0PKCs9ZtIfTa907MdIe2DqK7a9//Ss9++yzga42IqIVK1bQBx98YCY5TxKTAokoZh4AhnEFTj5PWudWaQQDAkl+rJ4YJLvjU+ReL3FlebnHR969Z+Y8egWCdN+CAmHOIDNlIu8ijKbnXuvaypdhiZGY1KgM82/x8DwIkRKzAomJD1gI24/bGxSFe6CmpkaYKDKSNcHsvK/U4iKNxiCFQ000aI3OUxvUEs4epa7JaI0e1rpHYzgmVW/7nWB0cdv29nY8+uij6N+/P7p164ba2loAwP33349ly5aZXDKXiQp+v7BStd/vtCWMk4grng8bJvzl+8EelFaNdxNJScIq9vKV1g8fNm+3Wpp60FM/DR0qrFgPAOPHCyvFy88r/g8I6bW2Gq/3pOmJdlVXK5eLuG9dnfbvamUivU/Ky4GRI4GpU4W8mSlHI2jdo/LfAOWyj2EMC6THH38cK1aswJNPPonk5OTA9lGjRuH111+31DjGQrhRZETc3nDHCnl53mxQcnI67S4oAHJz7T+n3vopKQkoKwOqqoANG9QFRGsrMGaMkF7PnsHpGnlRlNo1Zw5QWChsV7qe0utdVNR5LrV09+wRPrm5nceJbN0KlJbaX09r3aPy34YO1Vf2sYRR19TgwYNpzZo1RETUrVs32r9/PxER7d27l0ex2YVRl7HS/jEywRdjAdGOcYhn3N6VKbMvEIMU7RmTteonPV1U8rifkSOVg7alI3P15EspmF2tK6+tTfh9x47gSSjl66opxVK1tARPj2B2sVkzaMWbhYtjc/O9rYFtMUipqal08OBBIgoWSJWVldS1a1cTpnqTqAkko3EMSvuLD26kwYtM7ODhyo2REMl1VKgrAgIp2i9UaqI9XP2n9LvaqLbx49XXGTMYzB5yXmn8kdJs1FLbleyTxlI5OcxeT3lHupyVC7BNII0bN47+9Kc/EVGwQHr44YfpggsuMGGqN4maQDJaUWnNZaT0NhPvsFBgvEqkQeAKdUvQKDarvYxGPUEqNobLA7W1CfMkidvS0oh27lQeISZt6EeO7NzPqF1yUaMkkqQiSGEOqqDzWVn2Ruo4rSkGpHZFspyVC7BNIH3wwQeUmZlJS5YsofT0dHrqqafo5ptvpuTkZFq9erVpg72GIx4kPQ+L1DWu9sbECLh9lJGXYKEZfSL18ijULUHzIFnZvWL2WQtX/ykJnqoqQejo6bJT8uZIpw/Q61mSLwMi7zJTmi5AXNLJqvJV2j+SHgh5N5+8rMwuZ+UCbB3mv27dOiouLqazzz6b0tLS6Pzzz6dPP/3UlKFexZUxSHKF39LC8SZacFyWNbDQdAYrnm21GCS959Uz03S4VeoN2qj6u1SkSGfU1prLqK1NfYFWpVAFrVgdJcETjRcH6fWQ9xJoxVAplaGYF7VlTmJkIfOozIMUz7hyHiS1Bj/c20q8wuLRGlhoOofFDbAugaT3esu7kowsm2EmX3rEgJK4a2kJDupWmvNHKpjEl0+tNO0mnBdMbc08peVVlOxXqxtjxFNsm0AaNGgQffvttyHbv/vuOxo0aJDR5DyLKwWS1k3Nb/jKxMgD7ygsNGMGwx4kreut1HCLq9TrnTjRyu44JZvUXiLlackF06hR2l4aO5GXj9Rzppa3qir1cAv5djHuSKvbzuN1pm0CyefzUUNDQ8j2+vp6Sk5ONpqcZ3GlQCJSvnn5DZ+xmxioNBmdAolI3/WWe5Dkjbma+ImkvtLTHadXzIfrilMLuhZjmOxCKZA6XMyTWt7VvHxq1yZGXrb1tt8+IiI98yV99NFHAIDLL78cb775JjIzMwO/tbe3o7S0FJ999hmqqqqsmaDJ5TQ1NSEzMxONjY3IyMhw2hxtxMnOtm4VJvyKl0m+GIYxxL59+wAA+fn51iQoTsoICBMN1tYKky6KVFV1znotPcbO+srvF+wwOlN1a6tgz+7doXbt2SPMgC2ilC+rkJZPejpw6pQwoWNZmfC7Vt7kea+uDr4eUpTyIN/fznzaiN72W7dASkgQJt32+XyQH9KlSxcMHDgQzzzzDP7zP/8zArO9g6cEEmCuUjBbkTAM40l0CySzdYNe8SOmn5srLOHhljpILd/Rfgn1+4WZtmfO7NxmRqxI7S4qEvxI5eXqeYiRl23LBZLIoEGDsGXLFvTq1StiI72M5wSSUeQPTlmZJx8EhmH0o0sgRVo3KIkMtW1eqoO0RKPck2ZFPlpbhWVUTp0SPEnHjwOpqZHZDYS/NjHw4qy3/Ta8FtuBAwfiXhzFBOHWI4r39bp4YV+GUSbSukG+eKvaOmxO1EHS5178X+9it2qL0vr9wKRJQhfcyJHC/1bUK3V1gjgChL91debSUVroV+vaRLIgsccwLJAAYO3atbjsssuQn5+P/Px8zJo1C+vXr7faNsYu9CwMacVCm14VGbywL8OoY/UivGpCKNqL/Uqf+0mThI/SYrdGqa0Vuq1EysutEXvRKJ84f1E2LJDefvttFBcXIz09Hbfffjtuv/12pKWlYfr06fjLX/5ih42MWdQEip6bXu+q2Vrn9qrIiPNKgWE0ibRukKPW0EvPs26d8BxaVY8o1Y3S5768vFPUiF4as3VBXh5QWNj5vahI2BbpC6TV10FEale0RarbMDo87txzz6Vnn302ZPszzzxD5557rtHkPItrh/mLaA3HjMa8NV6eWoDn9WHiFN3D/K1Ga3i+1UPL1dJTm1TRyASXSrS0CDOJl5crz7NkNk92TK2hNmmkOFO50sSbHpzew7Z5kJKTkxUfoJqaGkpJSTGanGdxvUAKJ1DsvrG9LjJieJI0hlHDMYGkhdUvW1rpyec+UhMGemlp6RRY6emdM3BbuY6eVfMRqS0xonY+D8+JpLf9NtzFlpOTg9LS0pDta9asQU5OTsQeLUYHelyz4Vyjdgfa2eX+jRZK5ePlbkOGEfFabKBaXWY2H1p1o1LAcmqqsbpSatfatcGB1GvXhrdBD1aHAYh128yZwog4uV1K54uHUASjyuuVV16h5ORk+vnPf05vvfUWvfXWW3TLLbdQSkoKvfrqq6YVnddwzINkRLXrne3WTR4Rt9kjxcvdhgxDFLb+cKUHiUjwvIgzRhNpd5PpXdhbbz0jP3e4dKV2NTcHe5B27Og8p5F0tc5j1EOvZ7UF+XIj0hnQpbNwe7SXwNbFat977z06//zzqUePHtSjRw86//zz6YMPPjBl6EsvvUQDBgyglJQUmjBhAm3atEnXce+88w4BoNmzZwdtnzt3LgEI+syYMSNon+PHj9O1115L3bt3p8zMTPrZz35Gzc3Nhux2TCBZ2UgbdZFGs1vOqlgDK+31cIXAMEQUtv5wpUBSqheU8mFHl49aF5ncPrWFY0VxsWoV0bhxxpZc0VMuRus3PfFXSgJItJljkKLHypUrKTk5mZYvX06VlZU0f/58ysrKUlzrTcqBAweof//+NHXqVEWBNHPmTDp69Gjgc+LEiaB9Zs6cSeeddx5t3LiR1q9fT/n5+XTNNdcYst0VHqRIG2kjYisa/c1Oij8j6XqwQmAYIgpbf7hSIIUTQ2I+lBaNjfRZlcfklJQE/64kIsTvBQXqMUdqsT5aWFH36I2/Crevx7FNIM2bN48+//xzs3YFMWHCBLr11lsD39vb2yk7O5sWL16seozf76cpU6bQ66+/TnPnzlUUSPJtUvbs2UMAaMuWLYFtf//738nn89HXX3+t23ZHg7StaqSNiK1oPCxOiT+GiSc06g9bBVJbmyBaxJFcRo5Tqhfk+VAbhRaJd1zqQUpLI9q5U7trSvQYiQvbFhYK+ZV3USl1WektAzFNs14nvefV8p55/EXRNoE0a9YsSklJoXPOOYfuvPNO2r59uykDT58+TYmJifT+++8HbZ8zZw7NmjVL9bgHHniALr/8ciJSFkNz586lzMxMOvvss2no0KH085//nL799tvA78uWLaOsrKygY9ra2igxMZHee+891fO2trZSY2Nj4HP48GF3j2JTQ6lS0dtnH43uJSfEH8MwRGSjQGpri2zFe6OxRZWV1nnHxS4yJcGlx5Ol1kVlpK5TS9POrjm1l0wPj14TsbWL7cSJE/Taa6/RtGnTKCEhgUaMGEGPP/44HThwQHcaX3/9NQGgDRs2BG2/6667aMKECYrHrF+/nvr370/ffPMNESkLpHfeeYc+/PBD2rlzJ73//vs0fPhwGj9+PPn9fiIievzxx2no0KEhaZ999tn0yiuvqNr74IMPhsQ2eU4gRXpjKz1Ybn6TcLNtDONCbBNISg28nV5dq73jRrqmpOe2I2TAijT11I1qZRgD3nnbhvkDwFlnnYUFCxbgiy++wKFDh3DjjTfiT3/6U/gVoCOgubkZN9xwA5YuXaq5FtzVV1+NWbNmoaCgAJdffjk+/vhjbNmyBV988UVE51+0aBEaGxsDn8OHD0eUniNEaw0ltxBHawYxUcRrw+TdQF4eMG5c5/f0dCA3177zqU0zonTt9Ay5l+5TUBBsu7yeEc9dWdk5g7aeofxa95XZNNXOo6feVivDOJpd25RAEmlra8PWrVuxadMmHDx4EH369NF9bK9evZCYmIiGhoag7Q0NDejbt2/I/vv378fBgwdx2WWXISkpCUlJSXjrrbfw0UcfISkpCfv371c8T15eHnr16hVYpbpv3744duxY0D5+vx8nTpxQPK9ISkoKMjIygj6eI1prKDFMrCBvtNz+UuBWkpKAP/2p83ski6saOaeeFzo9c7YlJQHr1wOjRgG7dgFTp2pf+6QkYMQIYONGfXPB6bmvjKaphpF6W+kl0+tz3BnAlED6/PPPMX/+fPTp0wc33ngjMjIy8PHHH+Orr77SnUZycjIKCwuDJp3s6OhAaWkpJk+eHLL/ueeei127dqGioiLwmTVrFi6++GJUVFSoTlL51Vdf4fjx4+jXrx8AYPLkyTh58iTKJYsH/uMf/0BHRwcmTpyo235PEq01lJjowd4M+1BqtPilwDxDh5qrL6y6x7WunR5vc10dsHu38vFq6PViRypajGBFvR0v3nmjfXfZ2dmUmppKl19+Of31r3+l1tZW0/2AK1eupJSUFFqxYgXt2bOHFixYQFlZWVRfX09ERDfccAPde++9qsfLY5Cam5vpzjvvpLKyMjpw4ACtWbOGxo0bR0OGDAmyc+bMmTR27FjatGkTffnllzRkyBDvDPN3Gxzn4xwxECzpavQOL48hbB/mb7S+sPIel44uM3Pt9MyJpIVW3qN9X4m2RLKMioexLUj7j3/8I3333Xdm7QrhxRdfpNzcXEpOTqYJEybQxo0bA79NmzaN5s6dq3qsXCCdOnWKLrnkEjr77LOpS5cuNGDAAJo/f35AcIkcP36crrnmGurWrRtlZGTQTTfd5J2JIhlGJAaCJV2N3uHlMYSto9jMlJlV97j0Wo4aZW72ajO2iFMb7NgRftqBaN9XcfyCpbf99hEROevD8iZNTU3IzMxEY2OjN+ORGO8jdgFt3Sq4ymM8HsARxG61vLy4KFsxVtPSATfS+7SoSOjmN7KumRX3eHW10FUqUlUldBEZQa8t4j2TmwtccAEgCeeI6PxWY0WZeBS97XdEQdpMHMGxLu4jjoIlHSNeYi3sJJK4LavucaviJd98UxhJpiWOxLi18eOVxZF4fqfr1Ly8zhFxRUXaZeK0rQ7BAokJD4/ccS9WNOBxWvkxKlh9P0QqTqy4xyMVWmIdOHIkMHeu+n5SMbh7t7C/SGFhp7gC3FWnanUkxXH9zwKJCQ+P3Ild4rjyYxTw+4Gf/tTa+8Etns5IhJbeOlAuBrduFURRZaUwPH/ECOH8bqhTa2s7PVzl5eo2uMFWh2CBxISHh/PHLnFc+TEKHD5sfCi7HCUPlFNdlVZ5w/TWgXIxmJoqiCJRGBlNz060bJCWmxtsdQgWSEx43PIGyFhPHFd+jAI5OcJkiIBwP+TmGhMYbvJIWmmLkTpQjxh0Q52qNdu4tNwA5211CB7FZhIexcbEDHE2UotRZ9++fYDfj/yEBEEcTZ1qbPSZm0ZG6bElknvfiufGjc+em66hTfAoNoZh9MEjtRgp4v1QV2e8+9VNHslwthj1MEm7ncx6p6xIw27cdA0dhgVSPMCjlBiGMYqZhjIaXUd667NwthiJv5OLmepq4+LRijSigRu6/1wCC6RYx61vKQzDuBuzDaWdHkmj9ZmWLUYEoFxMAaHHhhNuetJwC+xVBsACKfbhUUqM1bBHMn5wW0NpZX1mRADKxdTQocHHAuGFW7g0rC5jfk4jhgWSV9F783N/MmMl7JFknMTq+kyvAFQSU9Jj9Qi3cGlYiVPPaYyJMhZIXsTIzc/9yYwe9FZs7JFknMTJ+syK7jqrBZHac+vEcxqDL08skLyI0ZvfbW5yxl0YqdjYI8mEw24vgp1eF712y/d1QrhpPbdOPKcx+PLEAsmLcCPFWImRio09kowWXvEiyAWOEbvV9o32i6jWc+vEcxqD7RILJC/CjZS78Vo/vNGKjT2SjBpe8CIoCRwjdrslj+Ge22g/pzHYLrFA8ip6bn6vNdSxgBveoI1e9xis2BiH8IIXQUng6F2XDDCWRzvrYKPPbTTagxh7eWKBFKu4oaGOR5x+uzR73WOsYmMcwgtiW0ng6F2XzO8P3nfdOuEZV3rO5Me2tlovUPQ+t9wemIIFUqzidEMdrzj9Bs3XnXEat4ttNTGkZLfa85SUJDzbU6eqiw75sePHOydQuF4wBQukWCUaDTV34YXi9Bu00wLN7fA9ywD6RZzW8xROdEiPLSgAdu9W39duuF4wBQukWMXuhppdtuo4+QbttEBzM3zPMkpoiWat50lPkLR47ObNzgoUrhdM4SMictoIL9LU1ITMzEw0NjYiIyPDaXOiT3W10NCIVFUJooBh3Arfs2HZt28fACA/P99hS6KEKJq3bhUEzPr1QF0dkJsr/BXjk7SOF4O89cQB6d2XsRW97Td7kBhzsMuW8Rp8zzJy1OKEevYM9jSKXqbWVmDPHuEjBmzr9Ra7PTaLCYEFUrwSaSwGu2yjB8fNWAPfs4wcqWgeOrQzTujUKeHv1q3Csyd2zfboAYwcKXwmTeJnMsZhgRSPWBWLwW9E9sNxM9bC92zsYuZFIilJ6FYbNUo4Nj1d2C7+HT9e+Ct6mVpaOo8tL+fRYDEOC6R4hId8ege+VkysYYdHNJIXibq6YM9RSQlw/Hinp3Ho0E4vU1pa53FFRdxNG+OwQIpHohWLwV1DkcNxM0wsYZdHNJIXCfkzNn06kJra6WmUds2eOAFUVgqfsjL2RMY4PIrNJJ4fxWb3iAr56BCuTMzDo1+YKGH7KDa7RhJK65vx443HmPEzFlfwKDZGG7tjMbhryDo4boaJFezyiEYagM/PGKMACyTGHrhriGEYOXaOJGSRw1gMCyTGHuyqCDmuiWG8jbiOmdoirwzjElggMfZhxRudVBDxkHeG8T78HFsPvzjaAgskxr3IK9Lqao5rYhivw/GJ+tArelhw2gYLJMa9yCtSgOOaGMbrcHxieIyIHnk9WV3N3iSLYIHEuBd5RTp0KC8VwTBeh5d8CY8RL5u0niwqAubMYW+SRfCdGQ94dY4PsSKV284rsDOMtxHjExllRNEjzuuk5WWT1pN+v7BOHNAprLicTcMepFjH6/3TTg3ddVvQo9vsYRjGPox62cR6UrosCndfRgwLpFiHAyKN4zZR6TZ7GIaxHzMvh9x9aSkskGIdDog0jttEpdvsYRjGvfCEmZbBAinW4TcK47hNVLrNHoZhmDjAcYH08ssvY+DAgUhNTcXEiROxefNmXcetXLkSPp8Pl19+eWBbW1sb7rnnHhQUFKBr167Izs7GnDlzcOTIkaBjBw4cCJ/PF/RZsmSJldlyF/xGYQy3iUq32SPCcVEMw8Qwjgqkd999FwsXLsSDDz6Ibdu24bzzzsOMGTNw7NgxzeMOHjyIO++8E1OnTg3afurUKWzbtg33338/tm3bhvfeew9VVVWYNWtWSBqPPPIIjh49Gvj88pe/tDRvMUE8N4BuE5Vus4fjohiGiXEcFUjPPvss5s+fj5tuugkjRozAq6++ivT0dCxfvlz1mPb2dlx33XV4+OGHkSfrasjMzMRnn32GK6+8EsOGDcOkSZPw0ksvoby8HHV1dUH7du/eHX379g18unbtqmnr6dOn0dTUFPSJabgBZLTguCiGYWIcxwTSmTNnUF5ejuLi4k5jEhJQXFyMsrIy1eMeeeQR9O7dG/PmzdN1nsbGRvh8PmRlZQVtX7JkCXr27ImxY8fiqaeegj+MAFi8eDEyMzMDn5ycHF3n9yzcADJacFwUwzAxjmP++m+//Rbt7e3o06dP0PY+ffrgX//6l+IxX375JZYtW4aKigpd52htbcU999yDa665BhkZGYHtt99+O8aNG4cePXpgw4YNWLRoEY4ePYpnn31WNa1FixZh4cKFge9NTU2xLZKMTFTGxB9qk3gyDMPECJ6p1Zqbm3HDDTdg6dKl6NWrV9j929racOWVV4KI8Ic//CHoN6nQGT16NJKTk3HLLbdg8eLFSElJUUwvJSVF9beYhBtAJhw8GzLDMDGMY61er169kJiYiIaGhqDtDQ0N6Nu3b8j++/fvx8GDB3HZZZcFtnV0dAAAkpKSUFVVhcGDBwPoFEeHDh3CP/7xjyDvkRITJ06E3+/HwYMHMWzYsEizFjtwAxiMV5dsYRiGYQzjWAxScnIyCgsLUVpaGtjW0dGB0tJSTJ48OWT/c889F7t27UJFRUXgM2vWLFx88cWoqKgIdHeJ4qimpgZr1qxBz549w9pSUVGBhIQE9O7d27oMMrEFB60zDGMX8Txi2MU4+hq8cOFCzJ07F0VFRZgwYQKef/55/PDDD7jpppsAAHPmzEH//v2xePFipKamYtSoUUHHi4HX4va2tjb813/9F7Zt24aPP/4Y7e3tqK+vBwD06NEDycnJKCsrw6ZNm3DxxReje/fuKCsrwx133IHrr78eZ511VvQyz3gLpaB19q4xDBMp4svX1q1C3GdZGXuoXYKjV+Gqq67CN998gwceeAD19fUYM2YMSkpKAoHbdXV1SEjQ7+T6+uuv8dFHHwEAxowZE/Tb559/josuuggpKSlYuXIlHnroIZw+fRqDBg3CHXfcERSXxDAhcNA6wzB2wC9frsVHROS0EV6kqakJmZmZaGxsDBvjxMQIHIPExDj79u0DAOTn5ztsSRwh9SCNH++u2fJjFL3tN18FhtELB60zDGM1PGLYtfCVYBiGYRgn4ZcvV+L4YrWMS+BRFAzDMAwTgAUSw0PYGYZhGEYGCySG111jGIZhGBkskBheeJRhGIZhZHCQNsOjKBiGYRhGBreEjACPomAYhmGYANzFxjAMwzAMI4MFEsMwyvDUDwzDxDEskBiGCYWnfmAYJs5hgcQwTCg89QPDMHEOCySGYULhqR8YholzeBQbwzCh8NQPDMPEOVzrMQyjDE/9wDBMHMNdbAzDMAzDMDJYIDEMwzAMw8hggcQwDMMwDCODBRLDMAzDMIwMFkgMwzAMwzAyWCAxDMMwDMPIYIHEMAzDMAwjgwUSwzAMwzCMDBZIDMMwDMMwMlggMQzDMAzDyGCBxHgfvx+orhb+xrMNDMMwjGWwQGK8jd8PTJ4MDBsm/HVCoLjBBoZhGMZSWCAx3qa2Fti6Vfh/61bhezzawDAMw1gKCyTG2+TlAUVFwv/jxwvf49EGhmEYxlKSnDaAYSIiKQkoKxO8Nnl5wvd4tIFhGIaxFK7JGe+TlAQMHco2MAzDMJbBXWxMeHiEVnTh8mYYhnEcFkiMNjxCK7pweTMMw7gCFkiMNjxCK7pweTMMw7gCFkiMNjxCK7pweTMMw7gCDtJmtOERWtGFy5thGMYVcO3LhIdHaEUXLm+GYRjH4S42hmEYhmEYGY4LpJdffhkDBw5EamoqJk6ciM2bN+s6buXKlfD5fLj88suDthMRHnjgAfTr1w9paWkoLi5GTU1N0D4nTpzAddddh4yMDGRlZWHevHn4/vvvrcoSE6vw8HuGYZi4wVGB9O6772LhwoV48MEHsW3bNpx33nmYMWMGjh07pnncwYMHceedd2Lq1Kkhvz355JN44YUX8Oqrr2LTpk3o2rUrZsyYgdbW1sA+1113HSorK/HZZ5/h448/xrp167BgwQLL88d4BD3Ch4ffMwzDxBU+IiKnTj5x4kSMHz8eL730EgCgo6MDOTk5+OUvf4l7771X8Zj29nZceOGF+NnPfob169fj5MmT+OCDDwAI3qPs7Gz85je/wZ133gkAaGxsRJ8+fbBixQpcffXV2Lt3L0aMGIEtW7ag6MfRQiUlJbj00kvx1VdfITs7W/G8p0+fxunTpwPfm5qakJOTg8bGRmRkZFhVJEy0EYXP1q3C6LGyMuXA6OpqQRyJVFVxnBATc+zbtw8AkJ+f77AlDGMfTU1NyMzMDNt+O+ZBOnPmDMrLy1FcXNxpTEICiouLUVZWpnrcI488gt69e2PevHkhvx04cAD19fVBaWZmZmLixImBNMvKypCVlRUQRwBQXFyMhIQEbNq0SfW8ixcvRmZmZuCTk5NjKL+MS9E77xAPv2cYhokrHBNI3377Ldrb29GnT5+g7X369EF9fb3iMV9++SWWLVuGpUuXKv4uHqeVZn19PXr37h30e1JSEnr06KF6XgBYtGgRGhsbA5/Dhw9rZ5DxBnqFjzj8vqoK2LCBh98zDMPEOJ6p5Zubm3HDDTdg6dKl6NWrV9TPn5KSgpSUlKifl7EZI/MO8fB7hmGYuMExgdSrVy8kJiaioaEhaHtDQwP69u0bsv/+/ftx8OBBXHbZZYFtHR0dAAQPUFVVVeC4hoYG9OvXLyjNMWPGAAD69u0bEgTu9/tx4sQJxfMycQALH4ZhGEaGY11sycnJKCwsRGlpaWBbR0cHSktLMXny5JD9zz33XOzatQsVFRWBz6xZs3DxxRejoqICOTk5GDRoEPr27RuUZlNTEzZt2hRIc/LkyTh58iTKy8sD+/zjH/9AR0cHJk6caGOOGYZhGIbxCo52sS1cuBBz585FUVERJkyYgOeffx4//PADbrrpJgDAnDlz0L9/fyxevBipqakYNWpU0PFZWVkAELT917/+NR577DEMGTIEgwYNwv3334/s7OzAfEnDhw/HzJkzMX/+fLz66qtoa2vDbbfdhquvvlp1BBvDMAzDMPGFowLpqquuwjfffIMHHngA9fX1GDNmDEpKSgJB1nV1dUhIMObkuvvuu/HDDz9gwYIFOHnyJC644AKUlJQgNTU1sM+f//xn3HbbbZg+fToSEhJwxRVX4IUXXrA0bwzDMAzDeBdH50HyMnrnUWAYhvEKPA8SEw+4fh4khmEYhmEYt8ICiWEYhmEYRgYLJIZhGIZhGBkskBiGYRiGYWSwQGIYhmEYhpHBAolhGIZhGEYGCySGYRiGYRgZLJAYhmEYhmFkODqTtpcR59dsampy2BKGYRhraG5uBsD1GhPbiPd3uHmyWSCZRKxIcnJyHLaEYRiGYRijNDc3IzMzU/V3XmrEJB0dHThy5Ai6d+8On8/nmB1NTU3IycnB4cOHecmTH+EyCYXLJBQuk1C4TELhMgkmFsqDiNDc3Izs7GzN9V7Zg2SShIQEnHPOOU6bESAjI8OzN6tdcJmEwmUSCpdJKFwmoXCZBOP18tDyHIlwkDbDMAzDMIwMFkgMwzAMwzAyWCB5nJSUFDz44INISUlx2hTXwGUSCpdJKFwmoXCZhMJlEkw8lQcHaTMMwzAMw8hgDxLDMAzDMIwMFkgMwzAMwzAyWCAxDMMwDMPIYIHEMAzDMAwjgwWSB3j55ZcxcOBApKamYuLEidi8ebPqvpWVlbjiiiswcOBA+Hw+PP/889EzNIoYKZOlS5di6tSpOOuss3DWWWehuLhYc3+vYqRM3nvvPRQVFSErKwtdu3bFmDFj8Kc//SmK1kYHI2UiZeXKlfD5fLj88svtNdABjJTJihUr4PP5gj6pqalRtNZ+jN4jJ0+exK233op+/fohJSUFQ4cOxSeffBIla6ODkTK56KKLQu4Rn8+Hn/zkJ1G02CaIcTUrV66k5ORkWr58OVVWVtL8+fMpKyuLGhoaFPffvHkz3XnnnfTOO+9Q37596bnnnouuwVHAaJlce+219PLLL9P27dtp7969dOONN1JmZiZ99dVXUbbcPoyWyeeff07vvfce7dmzh/bt20fPP/88JSYmUklJSZQttw+jZSJy4MAB6t+/P02dOpVmz54dHWOjhNEyeeONNygjI4OOHj0a+NTX10fZavswWh6nT5+moqIiuvTSS+nLL7+kAwcO0BdffEEVFRVRttw+jJbJ8ePHg+6P3bt3U2JiIr3xxhvRNdwGWCC5nAkTJtCtt94a+N7e3k7Z2dm0ePHisMcOGDAgJgVSJGVCROT3+6l79+705ptv2mVi1Im0TIiIxo4dS/fdd58d5jmCmTLx+/00ZcoUev3112nu3LkxJ5CMlskbb7xBmZmZUbIu+hgtjz/84Q+Ul5dHZ86ciZaJUSfSuuS5556j7t270/fff2+XiVGDu9hczJkzZ1BeXo7i4uLAtoSEBBQXF6OsrMxBy5zDijI5deoU2tra0KNHD7vMjCqRlgkRobS0FFVVVbjwwgvtNDVqmC2TRx55BL1798a8efOiYWZUMVsm33//PQYMGICcnBzMnj0blZWV0TDXdsyUx0cffYTJkyfj1ltvRZ8+fTBq1Cg88cQTaG9vj5bZtmJF/bps2TJcffXV6Nq1q11mRg0WSC7m22+/RXt7O/r06RO0vU+fPqivr3fIKmexokzuueceZGdnB1UCXsZsmTQ2NqJbt25ITk7GT37yE7z44ov493//d7vNjQpmyuTLL7/EsmXLsHTp0miYGHXMlMmwYcOwfPlyfPjhh3j77bfR0dGBKVOm4KuvvoqGybZipjxqa2vxt7/9De3t7fjkk09w//3345lnnsFjjz0WDZNtJ9L6dfPmzdi9ezduvvlmu0yMKklOG8Aw0WTJkiVYuXIlvvjii5gLNjVK9+7dUVFRge+//x6lpaVYuHAh8vLycNFFFzltWtRpbm7GDTfcgKVLl6JXr15Om+MaJk+ejMmTJwe+T5kyBcOHD8drr72GRx991EHLnKGjowO9e/fGH//4RyQmJqKwsBBff/01nnrqKTz44INOm+c4y5YtQ0FBASZMmOC0KZbAAsnF9OrVC4mJiWhoaAja3tDQgL59+zpklbNEUiZPP/00lixZgjVr1mD06NF2mhlVzJZJQkIC8vPzAQBjxozB3r17sXjx4pgQSEbLZP/+/Th48CAuu+yywLaOjg4AQFJSEqqqqjB48GB7jbYZK+qTLl26YOzYsdi3b58dJkYVM+XRr18/dOnSBYmJiYFtw4cPR319Pc6cOYPk5GRbbbabSO6RH374AStXrsQjjzxip4lRhbvYXExycjIKCwtRWloa2NbR0YHS0tKgt7p4wmyZPPnkk3j00UdRUlKCoqKiaJgaNay6Tzo6OnD69Gk7TIw6Rsvk3HPPxa5du1BRURH4zJo1CxdffDEqKiqQk5MTTfNtwYr7pL29Hbt27UK/fv3sMjNqmCmP888/H/v27QuIZwCorq5Gv379PC+OgMjukb/+9a84ffo0rr/+ervNjB5OR4kz2qxcuZJSUlJoxYoVtGfPHlqwYAFlZWUFhtrecMMNdO+99wb2P336NG3fvp22b99O/fr1ozvvvJO2b99ONTU1TmXBcoyWyZIlSyg5OZn+9re/BQ1HbW5udioLlmO0TJ544glavXo17d+/n/bs2UNPP/00JSUl0dKlS53KguUYLRM5sTiKzWiZPPzww/Tpp5/S/v37qby8nK6++mpKTU2lyspKp7JgKUbLo66ujrp370633XYbVVVV0ccff0y9e/emxx57zKksWI7Z5+aCCy6gq666Ktrm2goLJA/w4osvUm5uLiUnJ9OECRNo48aNgd+mTZtGc+fODXw/cOAAAQj5TJs2LfqG24iRMhkwYIBimTz44IPRN9xGjJTJb3/7W8rPz6fU1FQ666yzaPLkybRy5UoHrLYXI2UiJxYFEpGxMvn1r38d2LdPnz506aWX0rZt2xyw2j6M3iMbNmygiRMnUkpKCuXl5dHjjz9Ofr8/ylbbi9Ey+de//kUAaPXq1VG21F58REQOOa8YhmEYhmFcCccgMQzDMAzDyGCBxDAMwzAMI4MFEsMwDMMwjAwWSAzDMAzDMDJYIDEMwzAMw8hggcQwDMMwDCODBRLDMAzDMIwMFkgMwzAMwzAyWCAxDOMaLrroIvz6178OfB84cCCef/55y89z44034vLLL7c0zS+++AI+nw8nT560NF2GYZyBBRLDMK5ly5YtWLBgga59jYip3//+91ixYoWufQ8ePAifz4eKigpd+zMMExskOW0AwzCMGmeffbal6bW3t8Pn8yEzM9PSdBmGiT3Yg8QwjCP88MMPmDNnDrp164Z+/frhmWeeCdlH6hUiIjz00EPIzc1FSkoKsrOzcfvttwMQuuYOHTqEO+64Az6fDz6fDwCwYsUKZGVl4aOPPsKIESOQkpKCurq6kC62jo4OPPnkk8jPz0dKSgpyc3Px+OOPAwAGDRoEABg7dix8Ph8uuugi3Xn88ssvMXXqVKSlpSEnJwe33347fvjhh8Dvf/rTn1BUVITu3bujb9++uPbaa3Hs2LHA72K3XWlpKYqKipCeno4pU6agqqpKtw0Mw5iDBRLDMI5w1113Ye3atfjwww+xevVqfPHFF9i2bZvq/v/v//0/PPfcc3jttddQU1ODDz74AAUFBQCA9957D+eccw4eeeQRHD16FEePHg0cd+rUKfzud7/D66+/jsrKSvTu3Tsk7UWLFmHJkiW4//77sWfPHvzlL39Bnz59AACbN28GAKxZswZHjx7Fe++9pyt/+/fvx8yZM3HFFVdg586dePfdd/Hll1/itttuC+zT1taGRx99FDt27MAHH3yAgwcP4sYbbwxJ67e//S2eeeYZbN26FUlJSfjZz36mywaGYSKAGIZhokxzczMlJyfT//zP/wS2HT9+nNLS0uhXv/pVYNuAAQPoueeeIyKiZ555hoYOHUpnzpxRTFO6r8gbb7xBAKiioiJo+9y5c2n27NlERNTU1EQpKSm0dOlSxXQPHDhAAGj79u2aefr8888JAH333XdERDRv3jxasGBB0D7r16+nhIQEamlpUUxjy5YtBICam5uD0lyzZk1gn1WrVhEA1TQYhrEG9iAxDBN19u/fjzNnzmDixImBbT169MCwYcNUj/npT3+KlpYW5OXlYf78+Xj//ffh9/vDnis5ORmjR49W/X3v3r04ffo0pk+fbiwTYdixYwdWrFiBbt26BT4zZsxAR0cHDhw4AAAoLy/HZZddhtzcXHTv3h3Tpk0DANTV1QWlJbW/X79+ABDUFccwjPWwQGIYxhPk5OSgqqoKr7zyCtLS0vDf//3fuPDCC9HW1qZ5XFpaWiAmSe13O/j+++9xyy23oKKiIvDZsWMHampqMHjwYPzwww+YMWMGMjIy8Oc//xlbtmzB+++/DwA4c+ZMUFpdunQJ/C/mpaOjwxa7GYYRYIHEMEzUGTx4MLp06YJNmzYFtn333Xeorq7WPC4tLQ2XXXYZXnjhBXzxxRcoKyvDrl27AAieovb2dsO2DBkyBGlpaSgtLVX8PTk5GQAMpz1u3Djs2bMH+fn5IZ/k5GT861//wvHjx7FkyRJMnToV5557LnuFGMZF8DB/hmGiTrdu3TBv3jzcdddd6NmzJ3r37o3f/va3SEhQf2dbsWIF2tvbMXHiRKSnp+Ptt99GWloaBgwYAEAY8bZu3TpcffXVSElJQa9evXTZkpqainvuuQd33303kpOTcf755+Obb75BZWUl5s2bh969eyMtLQ0lJSU455xzkJqaqmuagHvuuQeTJk3Cbbfdhptvvhldu3bFnj178Nlnn+Gll15Cbm4ukpOT8eKLL+LnP/85du/ejUcffVRfATIMYzvsQWIYxhGeeuopTJ06FZdddhmKi4txwQUXoLCwUHX/rKwsLF26FOeffz5Gjx6NNWvW4H//93/Rs2dPAMAjjzyCgwcPYvDgwYbnT7r//vvxm9/8Bg888ACGDx+Oq666KuDNSUpKwgsvvIDXXnsN2dnZmD17tq40R48ejbVr16K6uhpTp07F2LFj8cADDyA7OxuAMMfTihUr8Ne//hUjRozAkiVL8PTTTxuym2EY+/ARETltBMMwDMMwjJtgDxLDMAzDMIwMFkgMwzAMwzAyWCAxDMMwDMPIYIHEMAzDMAwjgwUSwzAMwzCMDBZIDMMwDMMwMlggMQzDMAzDyGCBxDAMwzAMI4MFEsMwDMMwjAwWSAzDMAzDMDJYIDEMwzAMw8j4/wH+3BXRi4cDEwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TX has statewide lean of 0.53836 vs district avg lean 0.52825 for a turnout bias of 0.0101\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WI has statewide lean of 0.50077 vs district avg lean 0.49467 for a turnout bias of 0.00609\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute and visualize the turnout bias\")\n",
    "shortStateList = [\"AZ\", \"PA\", \"TX\", \"WI\"] #[\"AZ\", \"FL\", \"MI\",\"PA\", \"TX\", \"WI\"]  #too busy a plot\n",
    "plotList = [\"PA\",\"TX\"]\n",
    "MARKER, EC, COLOR = [\"o\",\"o\"], [\"red\",\"blue\"], [\"red\",\"blue\"]\n",
    "stateHDwts, stateHDleans, stateAvgHDlean, stateTrueLean, stateTotVotes = list(),list(), list(),list(),list()\n",
    "avgHDtotVote, stateHDmargins, stateTurnoutBias, stateHDvPops  = list(), list(), list(), list()\n",
    "for i,STATE in enumerate(shortStateList):\n",
    "    infile = \"./2024state_HD_output/\"+STATE+\"_HDstats2.csv\"\n",
    "    inDF = pd.read_csv(infile)\n",
    "    nDistricts = inDF[\"params\"][1]\n",
    "    stateHDvPops.append(inDF[\"HDvPop\"])\n",
    "    stateHDwts.append(inDF[\"HDweight\"])\n",
    "    stateHDleans.append(inDF[\"GOPlean\"])\n",
    "    stateHDmargins.append(inDF[\"voteMargin\"])\n",
    "    totDems, totReps = np.sum(inDF[\"vtdDems\"]),np.sum(inDF[\"vtdReps\"])\n",
    "    stateTotVotes.append(totDems + totReps)\n",
    "    avgHDtotVote.append( (totDems + totReps)/float(nDistricts) )\n",
    "    stateTrueLean.append(totReps / float(totDems+totReps) )\n",
    "    stateAvgHDlean.append(np.sum([stateHDwts[i][j] * stateHDleans[i][j] for j in range(len(stateHDwts[i])) ]) )\n",
    "    stateTurnoutBias.append(stateTrueLean[i] - stateAvgHDlean[i]) #see GrofmanKoetzleBrunell '97, p.464\n",
    "    print(STATE,\"has statewide lean of\",r5(stateTrueLean[i]),\"vs district avg lean\",r5(stateAvgHDlean[i]),\n",
    "          \"for a turnout bias of\",r5(stateTurnoutBias[i]) )\n",
    "    if STATE in plotList:\n",
    "        plotNo = plotList.index(STATE)\n",
    "        binStart = np.linspace(0.05, 0.95, 91)\n",
    "        nBins = len(binStart)\n",
    "        binTurnouts = [list() for b in range(nBins)]  #votes per district / \n",
    "        binHDs, binLeans, binMargins = [list() for b in range(nBins)], [list() for b in range(nBins)], [list() for b in range(nBins)]\n",
    "        dBin = (0.95 - 0.05) / float(91 - 1.)\n",
    "        binEnd = [binStart[j] + dBin for j in range(nBins)]\n",
    "        for j, lean in enumerate(stateHDleans[i]):\n",
    "            for b in range(nBins):\n",
    "                if lean >= binStart[b] and lean < binEnd[b] and stateHDwts[i][j] > 0:\n",
    "                    binHDs[b].append(j)\n",
    "                    binLeans[b].append(lean)\n",
    "                    binMargins[b].append(stateHDmargins[i][j] / avgHDtotVote[i])  #normalizing the margins\n",
    "                    HDsumVote = 0.\n",
    "                    if lean != 0.5:\n",
    "                        HDsumVote = stateHDmargins[i][j] / (2.*lean - 1.)\n",
    "                        binTurnouts[b].append(HDsumVote / float( stateHDvPops[i][j])  )\n",
    "                    else:  #0-weight district or tied (so, indeterminate total vote from lean and 0 margin)\n",
    "                        binTurnouts[b].append(avgHDtotVote[i] / stateHDvPops[i][j])\n",
    "                    break\n",
    "        binWts = [ list() ]\n",
    "        for b in range(nBins-1):\n",
    "            binWts.append(list())\n",
    "        #binWts = [list() for b in range(nBins) ]  #this is my convoluted way of countering the uneven HD weights in each bin\n",
    "        for b in range(nBins):  #we model each vote bin's distro of margins as normally distributed and fake-plot that\n",
    "            if len(binHDs[b]) > 0:\n",
    "                binWts[b] = [stateHDwts[i][j] for j in binHDs[b] ]\n",
    "                binMin, binMax = np.min(binTurnouts[b]), np.max(binTurnouts[b])\n",
    "                #binMin, binMax = np.min(binMargins[b]), np.max(binMargins[b])\n",
    "                binXmin, binXmax = np.min(binLeans[b]), np.max(binLeans[b])\n",
    "                nDots = int( round(1000 * np.sum(binWts[b]),0) )  #the no of fake-scattered dots scaled by how many HDs are binned here\n",
    "                binAvg,  binSD =  getWeightedAvgAndSD(binTurnouts[b], binWts[b])\n",
    "                #binAvg,  binSD =  getWeightedAvgAndSD(binMargins[b], binWts[b])\n",
    "                binXavg, binXsd = getWeightedAvgAndSD(binLeans[b],   binWts[b])\n",
    "                x,y = list(),list()\n",
    "                for dot in range(nDots):\n",
    "                    x.append( max(binXmin, min(binXmax,np.random.normal(binXavg, binXsd) ) ) )\n",
    "                    y.append( max(binMin,  min(binMax, np.random.normal(binAvg, binSD) ) ) )\n",
    "                if b == int(nBins/2):\n",
    "                    plt.scatter(x,y,marker = MARKER[plotNo],edgecolors = EC[plotNo],s=2,label=STATE,c=COLOR[plotNo])\n",
    "                else:\n",
    "                    plt.scatter(x,y,marker = MARKER[plotNo],edgecolors = EC[plotNo],s=2,c=COLOR[plotNo])\n",
    "                #print(\"I assigned\",len(binWts[b]), \"HDs to fall within\",binXmin,binXmax,\"min and max leans\")\n",
    "        plt.legend()\n",
    "        plt.xlabel(\"district lean\")\n",
    "        plt.ylabel(\"votes per district pop\")\n",
    "        #plt.ylabel(\"normalized district vote margin\")\n",
    "        #plt.axhline(1.0,ls=\"--\",dashes=(6,10),color=\"black\",lw=0.2)\n",
    "        #plt.axhline(-1.0,ls=\"--\",dashes=(6,10),color=\"black\",lw=0.2)\n",
    "        plt.axvline(0.5,lw=0.2, color=\"black\")\n",
    "        plt.axhline(0.5, ls=\"--\",dashes=(6,10),color=\"black\",lw=0.2)\n",
    "        #plt.plot([0.1,0.9],[-0.9,0.9],ls=\"--\",dashes=(12,12),lw=1)\n",
    "        plt.show()\n",
    "\n",
    "        \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e06b59d2-c2d2-497e-9b51-62c32409e4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(stateHDleans[i],weights = stateHDwts[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dfe5d01a-d3ff-456c-a757-8330f9b43f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x134e56a5510>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(binStart,[binEnd[b] - binStart[b] for b in range(nBins) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94f90d24-d15e-4b66-8dae-815bf2793894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"district lean\")\n",
    "plt.ylabel(\"normalized district vote margin\")\n",
    "plt.axhline(1.0,ls=\"--\",dashes=(6,10),color=\"black\",lw=0.2)\n",
    "plt.axhline(-1.0,ls=\"--\",dashes=(6,10),color=\"black\",lw=0.2)\n",
    "plt.axvline(0.5,lw=0.2, color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc7c463-fb8d-4945-a3b9-02cb90bbcfd9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6eb34497-1ab1-4bb4-ab3e-2ac29d0f40cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vote2seat(VOTE, NU=3.8, NUSIGMA=0.221): #default = Kenny values\n",
    "    return statsT.cdf(math.log(VOTE/(1.-VOTE)), NU,0,NUSIGMA)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ed7a33c-b89f-4255-867a-cd44510f7410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in np.linspace(0.1,0.9,100):\n",
    "    plt.scatter(i,abs(vote2seat(i) - i))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ea07cf4-549e-4d82-b5ea-1c34a8da7895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "if turnout is equal, we define geog bias as integ [HDwt * (S - V)) ]\n",
      "AZ has raw disproportionality of 0.02188\n",
      "AZ has corrected geography Bias 0.02316\n",
      "FL has raw disproportionality of 0.0399\n",
      "FL has corrected geography Bias 0.01296\n",
      "MI has raw disproportionality of 0.02917\n",
      "MI has corrected geography Bias 0.09087\n",
      "PA has raw disproportionality of 0.1113\n",
      "PA has corrected geography Bias 0.09822\n",
      "TX has raw disproportionality of 0.05155\n",
      "TX has corrected geography Bias -0.10169\n",
      "WI has raw disproportionality of -0.02785\n",
      "WI has corrected geography Bias 0.0028\n"
     ]
    }
   ],
   "source": [
    "nShortStates = len(shortStateList)\n",
    "print(\"if turnout is equal, we define geog bias as integ [HDwt * (S - V)) ]\")\n",
    "geogBias = [0.]*nShortStates\n",
    "nu, nuSigma = 3.8, 0.221  #again, our approximation to Kenny votes-to-seats convoluted model\n",
    "for n in range(nShortStates):\n",
    "    for i, LEAN in enumerate(stateHDleans[n]):\n",
    "        geogBias[n] += stateHDwts[n][i] * (vote2seat(LEAN) - LEAN )\n",
    "    print(shortStateList[n],\"has raw disproportionality of\",r5(geogBias[n]) ) \n",
    "    geogBias -= (vote2seat(stateAvgHDlean[n]) - stateAvgHDlean[n])   \n",
    "    print(shortStateList[n],\"has corrected geography Bias\",r5(geogBias[n]) )\n",
    "    \n",
    "#need to figure out why WI's geog bias is less GOP+ than expected.  Others look OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7dbd19c-a299-4462-81b7-d94e3ddfd068",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
